Wearable apparatus and methods for processing images to identify contextual situations

ABSTRACT

A wearable apparatus is provided for capturing and processing images from an environment of a user. In one implementation, the wearable apparatus is used for identifying a contextual situation related to a wearer. The wearable apparatus includes a wearable image sensor configured to capture a plurality of images from an environment of the wearer. The wearable apparatus further includes at least one processing device. The at least one processing device is programmed to analyze the plurality of images to identify the contextual situation related to the wearer; determine information associated with the contextual situation; and cause the transmitter to transmit the determined information to a device paired with the wearable apparatus to cause the paired device to provide at least one alert to the wearer based on the determined information associated with the contextual situation.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority of U.S. ProvisionalPatent Application No. 62/275,531, filed Jan. 6, 2016, which isincorporated herein by reference in its entirety.

BACKGROUND

Technical Field

This disclosure generally relates to devices and methods for capturingand processing images from an environment of a user, and usinginformation derived from captured images. More particularly, thisdisclosure relates to devices and methods for using a wearable deviceincluding a camera for capturing information related to the user'senvironment, and to systems for processing data received from thewearable device.

Background Information

Today, technological advancements make it possible for wearable devicesto automatically capture images and store information that is associatedwith the captured images. Certain devices have been used to digitallyrecord aspects and personal experiences of one's life in an exercisetypically called “lifelogging.” Some individuals log their life so theycan retrieve moments from past activities, for example, social events,trips, etc. Lifelogging may also have significant benefits in otherfields (e.g., business, fitness and healthcare, and social research).Lifelogging devices, while useful for tracking daily activities, may beimproved with capability to enhance one's interaction in his environmentwith feedback and other advanced functionality based on the analysis ofcaptured image data.

Even though users can capture images with their smartphones and somesmartphone applications can process the captured images, smartphones maynot be the best platform for serving as lifelogging apparatuses in viewof their size and design. Lifelogging apparatuses should be small andlight, so they can be easily worn. Moreover, with improvements in imagecapture devices, including wearable apparatuses, additionalfunctionality may be provided to assist users in navigating in andaround an environment, identifying persons and objects they encounter,and providing feedback to the users about their surroundings andactivities. Therefore, there is a need for apparatuses and methods forautomatically capturing and processing images to provide usefulinformation to users of the apparatuses, and for systems and methods toprocess and leverage information gathered by the apparatuses.

SUMMARY

Embodiments consistent with the present disclosure provide devices andmethods for automatically capturing and processing images from anenvironment of a user, and systems and methods for processinginformation related to images captured from the environment of the user.

Consistent with a disclosed embodiment, a wearable imaging device isprovided. The wearable imaging device may include an image capturedevice, a transmitter, and at least one processing device. The at leastone processing device may be programmed to: obtain at least one imagecaptured by the image capture device; analyze the at least one image todetect a contextual situation associated with the at least one image;based on the detected contextual situation, associate with the at leastone image a category tag, wherein the category tag is associated with aselected function; determine image-related information associated withthe detected contextual situation; and cause the transmitter to transmitthe determined image-related information to a device paired with thewearable imaging device to cause the paired device to execute theselected function based on the determined image-related information.

Consistent with another disclosed embodiment, another wearable imagingdevice is provided. This wearable imaging device may also include animage capture device, a transmitter, and at least one processing device.The at least one processing device may be programmed to: receive arequest from a device paired with the wearable imaging device totransmit information associated with a category tag, wherein thecategory tag is associated with a selected function; obtain at least oneimage captured by an image capture device included in the wearableimaging device; analyze the at least one image to detect a contextualsituation associated with the at least one image; determine, based onthe category tag, image-related information associated with the detectedcontextual situation; and cause the transmitter to transmit thedetermined image-related information to the paired device to cause thepaired device to execute the selected function based on the determinedimage-related information.

Consistent with another disclosed embodiment, a method is provided. Themethod may include: receiving a request from a device paired with awearable imaging device to transmit information associated with acategory tag, wherein the category tag is associated with a selectedfunction; obtaining at least one image captured by an image capturedevice included in the wearable imaging device; analyzing the at leastone image to detect a contextual situation associated with the at leastone image; determining image-related information associated with thedetected contextual situation; and transmitting the determinedimage-related information to the paired device to cause the paireddevice to execute the selected function based on the determinedimage-related information.

Consistent with another disclosed embodiment, a wearable apparatus forvisually pairing with an external device is disclosed. The wearableapparatus includes at least one transmitter, a memory, at least oneimage sensor configured to capture a stream of images from anenvironment of a user of the wearable apparatus, and at least oneprocessing device. The at least one processing device is programmed toreceive the stream of images from the at least one image sensor, analyzethe stream of images to detect the external device in the environment ofthe user, and cause the at least one transmitter to transmit aninterrogation signal, the interrogation signal being configured to causea change in at least one aspect of the external device. The at least oneprocessing device is further programmed to analyze the stream of imagesto detect the change in the at least one aspect of the external deviceand, after detection of the change in the at least one aspect of theexternal device, store in the memory information relating to theexternal device.

Consistent with another disclosed embodiment, a method for visuallypairing with an external device is disclosed. The method includesreceiving a stream of images captured from an environment of a user of awearable apparatus, analyzing the stream of images to detect theexternal device in the environment of the user, and causing at least onetransmitter associated with the wearable apparatus to transmit aninterrogation signal, the interrogation signal being configured to causea change in at least one aspect of the external device. The methodfurther includes analyzing the stream of images to detect the change inthe at least one aspect of the external device, and, after detection ofthe change in the at least one aspect of the external device, storinginformation relating to the external device.

Consistent with another disclosed embodiment, a system for controllingone or more controllable devices includes a transceiver and at least oneprocessing device. The processing device is programmed to obtain one ormore images captured by an image sensor included in a wearableapparatus, analyze the one or more images to identify a controllabledevice in an environment of a user of the wearable apparatus, analyzethe one or more images to detect a visual trigger associated with thecontrollable device and, based on the detection of the visual trigger,transmit, via the transceiver, a command. The command is configured tochange at least one aspect of the controllable device.

Consistent with another disclosed embodiment, a method for controllingone or more controllable devices includes obtaining one or more imagescaptured by an image sensor included in a wearable apparatus, analyzingthe one or more images to identify a controllable device in anenvironment of a user of the wearable apparatus, analyzing the one ormore images to detect a visual trigger associated with the controllabledevice, and, based on the detection of the visual trigger, transmittinga command. The command is configured to change at least one aspect ofthe controllable device.

Certain embodiments of the present disclosure relate to a server-basedsystem for interacting with a plurality of wearable apparatuses. Eachwearable apparatus may be associated with a different user. The systemmay include a data interface and at least one processing device. The atleast one processing device may be programmed to receive, via the datainterface and for each of the plurality of wearable apparatuses, a datastream including image-based information associated with images capturedby a camera present on a particular wearable apparatus from among theplurality of wearable apparatuses. The system may also analyze theimage-based information of the data streams received from each of theplurality of wearable apparatuses to determine at least one trait commonto two or more of the different users of the plurality of wearableapparatuses. The system may also store in a database informationrelating to the determined at least one trait.

Certain embodiments of the present disclosure also relate to a methodfor interacting with a plurality of wearable apparatuses. Each wearableapparatus may be associated with a different user. The method mayinclude receiving, for each of the plurality of wearable apparatuses, adata stream including image-based information associated with imagescaptured by a camera present on a particular wearable apparatus fromamong the plurality of wearable apparatuses. The method may also includeanalyzing the image-based information of the data streams received fromeach of the plurality of wearable apparatuses to determine at least onetrait common to two or more of the different users of the plurality ofwearable apparatuses. The method may further include storing in adatabase information relating to the determined at least one trait.

Consistent with a disclosed embodiment, a wearable apparatus is providedfor identifying a contextual situation related to a wearer. The wearableapparatus may include a wearable image sensor configured to capture aplurality of images from an environment of the wearer. The wearableapparatus may further include a transmitter and at least one processingdevice. The at least one processing device may be programmed to: analyzethe plurality of images to identify the contextual situation related tothe wearer; determine information associated with the contextualsituation; and cause the transmitter to transmit the determinedinformation to a device paired with the wearable apparatus to cause thepaired device to provide at least one alert to the wearer based on thedetermined information associated with the contextual situation.

Consistent with another disclosed embodiment, a method is provided foridentifying a contextual situation related to a wearer of a wearableapparatus. The method includes: receiving a plurality of images capturedfrom an environment of the wearer; analyzing the plurality of images toidentify the contextual situation related to the wearer; determininginformation associated with the contextual situation; and causing adevice paired with the wearable apparatus to provide at least one alertto the wearer based on the determined information associated with thecontextual situation.

Consistent with yet another disclosed embodiment, a software productstored on a non-transitory computer readable medium and comprising dataand computer implementable instructions for carrying a method foridentifying a contextual situation related to a wearer of a wearableapparatus, is provided. The method includes: receiving a plurality ofimages captured from an environment of the wearer; analyzing theplurality of images to identify the contextual situation related to thewearer; determining information associated with the contextualsituation; and presenting on a display at least one alert to the wearerbased on the determined information associated with the contextualsituation.

Certain embodiments of the present disclosure relate to a system forfacilitating collaboration between individuals. The system may include atransceiver and at least one processing device. The at least oneprocessing device may be programmed to obtain one or more imagescaptured by an image sensor included in a wearable apparatus. The atleast one processing device may also be programmed to analyze the one ormore images to detect a visual trigger in an environment of a wearer ofthe wearable apparatus. The visual trigger may be associated with acollaborative action to be taken. The at least one processing device mayalso be programmed to transmit, via the transceiver, an indicatorrelating to the visual trigger associated with the collaborative actionto be taken.

Certain embodiments of the present disclosure also relate to aserver-based system for facilitating collaboration among users of aplurality of wearable apparatuses. The system may comprise a datainterface and at least one processing device. The at least oneprocessing device may be programmed to receive, via the data interface,a data stream including image-based information associated with imagescaptured by a camera present on a particular wearable apparatus fromamong the plurality of wearable apparatuses. The at least one processingdevice may also be programmed to analyze the image-based information todetermine a visual trigger associated with a collaborative action to betaken. In some embodiments, the collaborative action may includedistributing information to two or more devices. And the at least oneprocessing device may also be programmed to distribute the informationto the two or more devices based on the visual trigger.

Certain embodiments of the present disclosure also relate to a methodfor facilitating collaboration between individuals. The method maycomprise obtaining one or more images captured by an image sensorincluded in a wearable apparatus. The method may also include analyzingthe one or more images to detect a visual trigger in an environment of awearer of the wearable apparatus. In some embodiments, the visualtrigger may be associated with a collaborative action to be taken. Themethod may also include transmitting an indicator relating to the visualtrigger associated with the collaborative action to be taken.

Certain embodiments of the present disclosure also relate to a methodfor facilitating collaboration among users of a plurality of wearableapparatuses. The method may include receiving a data stream includingimage-based information associated with images captured by a camerapresent on a particular wearable apparatus from among the plurality ofwearable apparatuses. The method may also include analyzing theimage-based information to determine a visual trigger associated with acollaborative action to be taken. In some embodiments, the collaborativeaction may include distributing information to two or more devices. Themethod may further include, based on the visual trigger, distributingthe information to the two or more devices.

In accordance with a disclosed embodiment, a wearable imaging apparatushaving variable privacy settings is provided. The apparatus may comprisea wearable image sensor configured to capture a plurality of images froman environment of a wearer of the wearable imaging apparatus, a memoryfor storing privacy mode triggers and associated privacy mode settings,and at least one processing device. The at least one processing devicemay be programmed to analyze the plurality of images and recognizewithin one or more of the plurality of images a presence of at least oneof the privacy mode triggers. Further, the processor device may beprogrammed to automatically cause one or more adjustments to thewearable imaging apparatus based on the privacy mode settings associatedwith the at least one recognized privacy mode trigger.

In accordance with another disclosed embodiment, a method for adjustingvariable privacy settings of a wearable imaging apparatus is provided.The method includes receiving a plurality of images captured from anenvironment of a wearer of the wearable imaging apparatus. The methodfurther includes analyzing the plurality of images and recognizingwithin one or more of the plurality of images a presence of at least oneprivacy mode trigger. Also, the method includes automatically causingone or more adjustments to the wearable imaging apparatus based on aprivacy mode setting associated with the at least one recognized privacymode trigger.

Consistent with other disclosed embodiments, non-transitorycomputer-readable storage media may store program instructions, whichare executed by at least one processor and perform any of the methodsdescribed herein.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various disclosed embodiments. Inthe drawings:

FIG. 1A is a schematic illustration of an example of a user wearing awearable apparatus according to a disclosed embodiment.

FIG. 1B is a schematic illustration of an example of the user wearing awearable apparatus according to a disclosed embodiment.

FIG. 1C is a schematic illustration of an example of the user wearing awearable apparatus according to a disclosed embodiment.

FIG. 1D is a schematic illustration of an example of the user wearing awearable apparatus according to a disclosed embodiment.

FIG. 2 is a schematic illustration of an example system consistent withthe disclosed embodiments.

FIG. 3A is a schematic illustration of an example of the wearableapparatus shown in FIG. 1A.

FIG. 3B is an exploded view of the example of the wearable apparatusshown in FIG. 3A.

FIG. 4A is a schematic illustration of an example of the wearableapparatus shown in FIG. 1B from a first viewpoint.

FIG. 4B is a schematic illustration of the example of the wearableapparatus shown in FIG. 1B from a second viewpoint.

FIG. 5A is a block diagram illustrating an example of the components ofa wearable apparatus according to a first embodiment.

FIG. 5B is a block diagram illustrating an example of the components ofa wearable apparatus according to a second embodiment.

FIG. 5C is a block diagram illustrating an example of the components ofa wearable apparatus according to a third embodiment.

FIG. 6 illustrates an exemplary embodiment of a memory containingsoftware modules consistent with the present disclosure.

FIG. 7 is a schematic illustration of an embodiment of a wearableapparatus including an orientable image capture unit.

FIG. 8 is a schematic illustration of an embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure.

FIG. 9 is a schematic illustration of a user wearing a wearableapparatus consistent with an embodiment of the present disclosure.

FIG. 10 is a schematic illustration of an embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure.

FIG. 11 is a schematic illustration of an embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure.

FIG. 12 is a schematic illustration of an embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure.

FIG. 13 is a schematic illustration of an embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure.

FIG. 14 is a schematic illustration of an embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure.

FIG. 15 is a schematic illustration of an embodiment of a wearableapparatus power unit including a power source.

FIG. 16 is a schematic illustration of an exemplary embodiment of awearable apparatus including protective circuitry.

FIG. 17A is a schematic illustration of a first example of a contextualsituation that triggers a device paired with a wearable apparatus toexecute a selected function according to a disclosed embodiment.

FIG. 17B is a schematic illustration of a second example of a contextualsituation that triggers the paired device to execute a selected functionaccording to a disclosed embodiment.

FIG. 17C is a schematic illustration of a third example of a contextualsituation that triggers the paired device to execute a selected functionaccording to a disclosed embodiment.

FIG. 17D is a schematic illustration of a fourth example of a contextualsituation that triggers the paired device to execute a selected functionaccording to a disclosed embodiment.

FIG. 18A is a message flow diagram that depicts the communicationbetween a wearable apparatus and a paired device for three differenttypes of selected functions consistent with disclosed embodiments.

FIG. 18B is a schematic illustration of three different types ofscenarios where multiple selected functions are triggered consistentwith disclosed embodiments.

FIG. 19 is a flowchart showing an exemplary process for causing a devicepaired with a wearable apparatus to execute a selected functionconsistent with disclosed embodiments.

FIG. 20 is a block diagram illustrating an example of the components awearable apparatus for visually pairing with an external device.

FIG. 21 is a flowchart illustrating an exemplary process for visuallypairing with an external device.

FIGS. 22A, 22B, and 22C are schematic illustrations of an exemplaryvisual pairing between a wearable apparatus and an external device.

FIG. 23 is a block diagram illustrating an example of the components ofa wearable apparatus for controlling an external device.

FIG. 24 is a flowchart illustrating an exemplary process for controllingan external device.

FIGS. 25A, 25B, and 25C are schematic illustrations of examples showingan external device being controlled by a wearable apparatus.

FIG. 26 illustrates an example environment consistent with the disclosedembodiments.

FIG. 27A illustrates an exemplary embodiment of a memory containingsoftware modules consistent with the present disclosure.

FIG. 27B illustrates an exemplary embodiment of a data stream consistentwith the present disclosure.

FIG. 28 is a flowchart illustrating an exemplary method of determining atrait consistent with the disclosed embodiments.

FIG. 29A is a schematic illustration of a first example of a contextualsituation that triggers provisioning of an alert according to adisclosed embodiment.

FIG. 29B is a schematic illustration of a second example of a contextualsituation that triggers provisioning of an alert according to adisclosed embodiment.

FIG. 29C is a schematic illustration of a third example of a contextualsituation that triggers provisioning of an alert according to adisclosed embodiment.

FIG. 29D is a schematic illustration of a fourth example of a contextualsituation that triggers provisioning of an alert according to adisclosed embodiment.

FIG. 30 is a flow diagram showing an exemplary process for providingalerts to a user consistent with disclosed embodiments.

FIG. 31 is a flowchart showing an exemplary process for providing alertsto a user consistent with disclosed embodiments.

FIG. 32A illustrates an example collaborative environment consistentwith the disclosed embodiments.

FIG. 32B illustrates an exemplary hand gesture as a visual triggerassociated with a collaborative action consistent with the disclosedembodiments.

FIG. 32C illustrates an example collaborative environment consistentwith the disclosed embodiments.

FIG. 33A illustrates an exemplary visual trigger associated with acollaborative action consistent with the disclosed embodiments.

FIG. 33B illustrates an exemplary visual trigger associated with acollaborative action consistent with the disclosed embodiments.

FIG. 33C illustrates an example environment consistent with thedisclosed embodiments.

FIG. 33D illustrates an exemplary visual trigger associated with acollaborative action consistent with the disclosed embodiments.

FIG. 34 illustrates an exemplary embodiment of a memory containingsoftware modules consistent with the present disclosure.

FIG. 35 is a flowchart illustrating an exemplary method of determining avisual trigger consistent with the disclosed embodiments.

FIG. 36A is a block diagram illustrating an example of the components ofa wearable apparatus according to a fourth embodiment.

FIG. 36B is a block diagram illustrating an example of the components ofa wearable apparatus according to a fifth embodiment.

FIG. 36C is a block diagram illustrating an example of the components ofa wearable apparatus according to a sixth embodiment.

FIG. 37 is a block diagram of an exemplary memory of a wearableapparatus storing software modules and at least one database.

FIGS. 38A, 38B, and 38C are example illustrations of image data capturedby an image sensor associated with a wearable apparatus, consistent withdisclosed embodiments.

FIG. 39 is an example of a process for automatically varying settingsassociated with a privacy mode for an image sensor associated with awearable apparatus, consistent with disclosed embodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions or modifications may be made to thecomponents illustrated in the drawings, and the illustrative methodsdescribed herein may be modified by substituting, reordering, removing,or adding steps to the disclosed methods. Accordingly, the followingdetailed description is not limited to the disclosed embodiments andexamples. Instead, the proper scope is defined by the appended claims.

FIG. 1A illustrates a user 100 wearing an apparatus 110 that isphysically connected (or integral) to glasses 130, consistent with thedisclosed embodiments. Glasses 130 may be prescription glasses,magnifying glasses, non-prescription glasses, safety glasses,sunglasses, etc. Additionally, in some embodiments, glasses 130 mayinclude parts of a frame and earpieces, nosepieces, etc., and one or nolenses. Thus, in some embodiments, glasses 130 may function primarily tosupport apparatus 110, and/or an augmented reality display device orother optical display device. In some embodiments, apparatus 110 mayinclude an image sensor (not shown in FIG. 1A) for capturing real-timeimage data of the field-of-view of user 100. The term “image data”includes any form of data retrieved from optical signals in thenear-infrared, infrared, visible, and ultraviolet spectrums. The imagedata may include video clips and/or photographs.

In some embodiments, apparatus 110 may communicate wirelessly or via awire with a computing device 120. In some embodiments, computing device120 may include, for example, a smartphone, or a tablet, or a dedicatedprocessing unit, which may be portable (e.g., can be carried in a pocketof user 100). Although shown in FIG. 1A as an external device, in someembodiments, computing device 120 may be provided as part of wearableapparatus 110 or glasses 130, whether integral thereto or mountedthereon. In some embodiments, computing device 120 may be included in anaugmented reality display device or optical head mounted displayprovided integrally or mounted to glasses 130. In other embodiments,computing device 120 may be provided as part of another wearable orportable apparatus of user 100 including a wrist-strap, amultifunctional watch, a button, a clip-on, etc. And in otherembodiments, computing device 120 may be provided as part of anothersystem, such as an on-board automobile computing or navigation system. Aperson skilled in the art can appreciate that different types ofcomputing devices and arrangements of devices may implement thefunctionality of the disclosed embodiments. Accordingly, in otherimplementations, computing device 120 may include a Personal Computer(PC), laptop, an Internet server, etc.

FIG. 1B illustrates user 100 wearing apparatus 110 that is physicallyconnected to a necklace 140, consistent with a disclosed embodiment.Such a configuration of apparatus 110 may be suitable for users that donot wear glasses some or all of the time. In this embodiment, user 100can easily wear apparatus 110, and take it off.

FIG. 1C illustrates user 100 wearing apparatus 110 that is physicallyconnected to a belt 150, consistent with a disclosed embodiment. Such aconfiguration of apparatus 110 may be designed as a belt buckle.Alternatively, apparatus 110 may include a clip for attaching to variousclothing articles, such as belt 150, or a vest, a pocket, a collar, acap or hat or other portion of a clothing article.

FIG. 1D illustrates user 100 wearing apparatus 110 that is physicallyconnected to a wrist strap 160, consistent with a disclosed embodiment.Although the aiming direction of apparatus 110, according to thisembodiment, may not match the field-of-view of user 100, apparatus 110may include the ability to identify a hand-related trigger based on thetracked eye movement of a user 100 indicating that user 100 is lookingin the direction of the wrist strap 160. Wrist strap 160 may alsoinclude an accelerometer, a gyroscope, or other sensor for determiningmovement or orientation of a user's 100 hand for identifying ahand-related trigger.

FIG. 2 is a schematic illustration of an exemplary system 200 includinga wearable apparatus 110, worn by user 100, and an optional computingdevice 120 and/or a server 250 capable of communicating with apparatus110 via a network 240, consistent with disclosed embodiments. In someembodiments, apparatus 110 may capture and analyze image data, identifya hand-related trigger present in the image data, and perform an actionand/or provide feedback to a user 100, based at least in part on theidentification of the hand-related trigger. In some embodiments,optional computing device 120 and/or server 250 may provide additionalfunctionality to enhance interactions of user 100 with his or herenvironment, as described in greater detail below.

According to the disclosed embodiments, apparatus 110 may include animage sensor system 220 for capturing real-time image data of thefield-of-view of user 100. In some embodiments, apparatus 110 may alsoinclude a processing unit 210 for controlling and performing thedisclosed functionality of apparatus 110, such as to control the captureof image data, analyze the image data, and perform an action and/oroutput a feedback based on a hand-related trigger identified in theimage data. According to the disclosed embodiments, a hand-relatedtrigger may include a gesture performed by user 100 involving a portionof a hand of user 100. Further, consistent with some embodiments, ahand-related trigger may include a wrist-related trigger. Additionally,in some embodiments, apparatus 110 may include a feedback outputtingunit 230 for producing an output of information to user 100.

As discussed above, apparatus 110 may include an image sensor 220 forcapturing image data. The term “image sensor” refers to a device capableof detecting and converting optical signals in the near-infrared,infrared, visible, and ultraviolet spectrums into electrical signals.The electrical signals may be used to form an image or a video stream(i.e. image data) based on the detected signal. The term “image data”includes any form of data retrieved from optical signals in thenear-infrared, infrared, visible, and ultraviolet spectrums. Examples ofimage sensors may include semiconductor charge-coupled devices (CCD),active pixel sensors in complementary metal-oxide-semiconductor (CMOS),or N-type metal-oxide-semiconductor (NMOS, Live MOS). In some cases,image sensor 220 may be part of a camera included in apparatus 110.

Apparatus 110 may also include a processor 210 for controlling imagesensor 220 to capture image data and for analyzing the image dataaccording to the disclosed embodiments. As discussed in further detailbelow with respect to FIG. 5A, processor 210 may include a “processingdevice” for performing logic operations on one or more inputs of imagedata and other data according to stored or accessible softwareinstructions providing desired functionality. In some embodiments,processor 210 may also control feedback outputting unit 230 to providefeedback to user 100 including information based on the analyzed imagedata and the stored software instructions. As the term is used herein, a“processing device” may access memory where executable instructions arestored or, in some embodiments, a “processing device” itself may includeexecutable instructions (e.g., stored in memory included in theprocessing device).

In some embodiments, the information or feedback information provided touser 100 may include time information. The time information may includeany information related to a current time of day and, as describedfurther below, may be presented in any sensory perceptive manner. Insome embodiments, time information may include a current time of day ina preconfigured format (e.g., 2:30 pm or 14:30). Time information mayinclude the time in the user's current time zone (e.g., based on adetermined location of user 100), as well as an indication of the timezone and/or a time of day in another desired location. In someembodiments, time information may include a number of hours or minutesrelative to one or more predetermined times of day. For example, in someembodiments, time information may include an indication that three hoursand fifteen minutes remain until a particular hour (e.g., until 6:00pm), or some other predetermined time. Time information may also includea duration of time passed since the beginning of a particular activity,such as the start of a meeting or the start of a jog, or any otheractivity. In some embodiments, the activity may be determined based onanalyzed image data. In other embodiments, time information may alsoinclude additional information related to a current time and one or moreother routine, periodic, or scheduled events. For example, timeinformation may include an indication of the number of minutes remaininguntil the next scheduled event, as may be determined from a calendarfunction or other information retrieved from computing device 120 orserver 250, as discussed in further detail below.

Feedback outputting unit 230 may include one or more feedback systemsfor providing the output of information to user 100. In the disclosedembodiments, the audible or visual feedback may be provided via any typeof connected audible or visual system or both. Feedback of informationaccording to the disclosed embodiments may include audible feedback touser 100 (e.g., using a Bluetooth™ or other wired or wirelesslyconnected speaker, or a bone conduction headphone). Feedback outputtingunit 230 of some embodiments may additionally or alternatively produce avisible output of information to user 100, for example, as part of anaugmented reality display projected onto a lens of glasses 130 orprovided via a separate heads up display in communication with apparatus110, such as a display 260 provided as part of computing device 120,which may include an onboard automobile heads up display, an augmentedreality device, a virtual reality device, a smartphone, PC, table, etc.

The term “computing device” refers to a device including a processingunit and having computing capabilities. Some examples of computingdevice 120 include a PC, laptop, tablet, or other computing systems suchas an on-board computing system of an automobile, for example, eachconfigured to communicate directly with apparatus 110 or server 250 overnetwork 240. Another example of computing device 120 includes asmartphone having a display 260. In some embodiments, computing device120 may be a computing system configured particularly for apparatus 110,and may be provided integral to apparatus 110 or tethered thereto.Apparatus 110 can also connect to computing device 120 over network 240via any known wireless standard (e.g., Wi-Fi, Bluetooth®, etc.), as wellas near-filed capacitive coupling, and other short range wirelesstechniques, or via a wired connection. In an embodiment in whichcomputing device 120 is a smartphone, computing device 120 may have adedicated application installed therein. For example, user 100 may viewon display 260 data (e.g., images, video clips, extracted information,feedback information, etc.) that originate from or are triggered byapparatus 110. In addition, user 100 may select part of the data forstorage in server 250.

Network 240 may be a shared, public, or private network, may encompass awide area or local area, and may be implemented through any suitablecombination of wired and/or wireless communication networks. Network 240may further comprise an intranet or the Internet. In some embodiments,network 240 may include short range or near-field wireless communicationsystems for enabling communication between apparatus 110 and computingdevice 120 provided in close proximity to each other, such as on or neara user's person, for example. Apparatus 110 may establish a connectionto network 240 autonomously, for example, using a wireless module (e.g.,Wi-Fi, cellular). In some embodiments, apparatus 110 may use thewireless module when being connected to an external power source, toprolong battery life. Further, communication between apparatus 110 andserver 250 may be accomplished through any suitable communicationchannels, such as, for example, a telephone network, an extranet, anintranet, the Internet, satellite communications, off-linecommunications, wireless communications, transponder communications, alocal area network (LAN), a wide area network (WAN), and a virtualprivate network (VPN).

As shown in FIG. 2, apparatus 110 may transfer or receive data to/fromserver 250 via network 240. In the disclosed embodiments, the data beingreceived from server 250 and/or computing device 120 may includenumerous different types of information based on the analyzed imagedata, including information related to a commercial product, or aperson's identity, an identified landmark, and any other informationcapable of being stored in or accessed by server 250. In someembodiments, data may be received and transferred via computing device120. Server 250 and/or computing device 120 may retrieve informationfrom different data sources (e.g., a user specific database or a user'ssocial network account or other account, the Internet, and other managedor accessible databases) and provide information to apparatus 110related to the analyzed image data and a recognized trigger according tothe disclosed embodiments. In some embodiments, calendar-relatedinformation retrieved from the different data sources may be analyzed toprovide certain time information or a time-based context for providingcertain information based on the analyzed image data.

An example of wearable apparatus 110 incorporated with glasses 130according to some embodiments (as discussed in connection with FIG. 1A)is shown in greater detail in FIG. 3A. In some embodiments, apparatus110 may be associated with a structure (not shown in FIG. 3A) thatenables easy detaching and reattaching of apparatus 110 to glasses 130.In some embodiments, when apparatus 110 attaches to glasses 130, imagesensor 220 acquires a set aiming direction without the need fordirectional calibration. The set aiming direction of image sensor 220may substantially coincide with the field-of-view of user 100. Forexample, a camera associated with image sensor 220 may be installedwithin apparatus 110 in a predetermined angle in a position facingslightly downwards (e.g., 5-15 degrees from the horizon). Accordingly,the set aiming direction of image sensor 220 may substantially match thefield-of-view of user 100.

FIG. 3B is an exploded view of the components of the embodimentdiscussed regarding FIG. 3A. Attaching apparatus 110 to glasses 130 maytake place in the following way. Initially, a support 310 may be mountedon glasses 130 using a screw 320, in the side of support 310. Then,apparatus 110 may be clipped on support 310 such that it is aligned withthe field-of-view of user 100. The term “support” includes any device orstructure that enables detaching and reattaching of a device including acamera to a pair of glasses or to another object (e.g., a helmet).Support 310 may be made from plastic (e.g., polycarbonate), metal (e.g.,aluminum), or a combination of plastic and metal (e.g., carbon fibergraphite). Support 310 may be mounted on any kind of glasses (e.g.,eyeglasses, sunglasses, 3D glasses, safety glasses, etc.) using screws,bolts, snaps, or any fastening means used in the art.

In some embodiments, support 310 may include a quick release mechanismfor disengaging and reengaging apparatus 110. For example, support 310and apparatus 110 may include magnetic elements. As an alternativeexample, support 310 may include a male latch member and apparatus 110may include a female receptacle. In other embodiments, support 310 canbe an integral part of a pair of glasses, or sold separately andinstalled by an optometrist. For example, support 310 may be configuredfor mounting on the arms of glasses 130 near the frame front, but beforethe hinge. Alternatively, support 310 may be configured for mounting onthe bridge of glasses 130.

In some embodiments, apparatus 110 may be provided as part of a glassesframe 130, with or without lenses. Additionally, in some embodiments,apparatus 110 may be configured to provide an augmented reality displayprojected onto a lens of glasses 130 (if provided), or alternatively,may include a display for projecting time information, for example,according to the disclosed embodiments. Apparatus 110 may include theadditional display or alternatively, may be in communication with aseparately provided display system that may or may not be attached toglasses 130.

In some embodiments, apparatus 110 may be implemented in a form otherthan wearable glasses, as described above with respect to FIGS. 1B-1D,for example. FIG. 4A is a schematic illustration of an example of anadditional embodiment of apparatus 110 from a first viewpoint. Theviewpoint shown in FIG. 4A is from the front of apparatus 110. Apparatus110 includes an image sensor 220, a clip (not shown), a function button(not shown) and a hanging ring 410 for attaching apparatus 110 to, forexample, necklace 140, as shown in FIG. 1B. When apparatus 110 hangs onnecklace 140, the aiming direction of image sensor 220 may not fullycoincide with the field-of-view of user 100, but the aiming directionwould still correlate with the field-of-view of user 100.

FIG. 4B is a schematic illustration of the example of a secondembodiment of apparatus 110, from a second viewpoint. The viewpointshown in FIG. 4B is from a side orientation of apparatus 110. Inaddition to hanging ring 410, as shown in FIG. 4B, apparatus 110 mayfurther include a clip 420. User 100 can use clip 420 to attachapparatus 110 to a shirt or belt 150, as illustrated in FIG. 1C. Clip420 may provide an easy mechanism for disengaging and reengagingapparatus 110 from different articles of clothing. In other embodiments,apparatus 110 may include a female receptacle for connecting with a malelatch of a car mount or universal stand.

In some embodiments, apparatus 110 includes a function button 430 forenabling user 100 to provide input to apparatus 110. Function button 430may accept different types of tactile input (e.g., a tap, a click, adouble-click, a long press, a right-to-left slide, a left-to-rightslide). In some embodiments, each type of input may be associated with adifferent action. For example, a tap may be associated with the functionof taking a picture, while a right-to-left slide may be associated withthe function of recording a video.

The example embodiments discussed above with respect to FIGS. 3A, 3B,4A, and 4B are not limiting. In some embodiments, apparatus 110 may beimplemented in any suitable configuration for performing the disclosedmethods. For example, referring back to FIG. 2, the disclosedembodiments may implement an apparatus 110 according to anyconfiguration including an image sensor 220 and a processor unit 210 toperform image analysis and for communicating with a feedback unit 230.

FIG. 5A is a block diagram illustrating the components of apparatus 110according to an example embodiment. As shown in FIG. 5A, and assimilarly discussed above, apparatus 110 includes an image sensor 220, amemory 550, a processor 210, a feedback outputting unit 230, a wirelesstransceiver 530, and a mobile power source 520. In other embodiments,apparatus 110 may also include buttons, other sensors such as amicrophone, and inertial measurements devices such as accelerometers,gyroscopes, magnetometers, temperature sensors, color sensors, lightsensors, etc. Apparatus 110 may further include a data port 570 and apower connection 510 with suitable interfaces for connecting with anexternal power source or an external device (not shown).

Processor 210, depicted in FIG. 5A, may include any suitable processingdevice. The term “processing device” includes any physical device havingan electric circuit that performs a logic operation on input or inputs.For example, processing device may include one or more integratedcircuits, microchips, microcontrollers, microprocessors, all or part ofa central processing unit (CPU), graphics processing unit (GPU), digitalsignal processor (DSP), field-programmable gate array (FPGA), or othercircuits suitable for executing instructions or performing logicoperations. The instructions executed by the processing device may, forexample, be pre-loaded into a memory integrated with or embedded intothe processing device or may be stored in a separate memory (e.g.,memory 550). Memory 550 may comprise a Random Access Memory (RAM), aRead-Only Memory (ROM), a hard disk, an optical disk, a magnetic medium,a flash memory, other permanent, fixed, or volatile memory, or any othermechanism capable of storing instructions.

Although, in the embodiment illustrated in FIG. 5A, apparatus 110includes one processing device (e.g., processor 210), apparatus 110 mayinclude more than one processing device. Each processing device may havea similar construction, or the processing devices may be of differingconstructions that are electrically connected or disconnected from eachother. For example, the processing devices may be separate circuits orintegrated in a single circuit. When more than one processing device isused, the processing devices may be configured to operate independentlyor collaboratively. The processing devices may be coupled electrically,magnetically, optically, acoustically, mechanically or by other meansthat permit them to interact.

In some embodiments, processor 210 may process a plurality of imagescaptured from the environment of user 100 to determine differentparameters related to capturing subsequent images. For example,processor 210 can determine, based on information derived from capturedimage data, a value for at least one of the following: an imageresolution, a compression ratio, a cropping parameter, frame rate, afocus point, an exposure time, an aperture size, and a lightsensitivity. The determined value may be used in capturing at least onesubsequent image. Additionally, processor 210 can detect imagesincluding at least one hand-related trigger in the environment of theuser and perform an action and/or provide an output of information to auser via feedback outputting unit 230.

In another embodiment, processor 210 can change the aiming direction ofimage sensor 220. For example, when apparatus 110 is attached with clip420, the aiming direction of image sensor 220 may not coincide with thefield-of-view of user 100. Processor 210 may recognize certainsituations from the analyzed image data and adjust the aiming directionof image sensor 220 to capture relevant image data. For example, in oneembodiment, processor 210 may detect an interaction with anotherindividual and sense that the individual is not fully in view, becauseimage sensor 220 is tilted down. Responsive thereto, processor 210 mayadjust the aiming direction of image sensor 220 to capture image data ofthe individual. Other scenarios are also contemplated where processor210 may recognize the need to adjust an aiming direction of image sensor220.

In some embodiments, processor 210 may communicate data tofeedback-outputting unit 230, which may include any device configured toprovide information to a user 100. Feedback outputting unit 230 may beprovided as part of apparatus 110 (as shown) or may be provided externalto apparatus 110 and communicatively coupled thereto.Feedback-outputting unit 230 may be configured to output visual ornonvisual feedback based on signals received from processor 210, such aswhen processor 210 recognizes a hand-related trigger in the analyzedimage data.

The term “feedback” refers to any output or information provided inresponse to processing at least one image in an environment. In someembodiments, as similarly described above, feedback may include anaudible or visible indication of time information, detected text ornumerals, the value of currency, a branded product, a person's identity,the identity of a landmark or other environmental situation or conditionincluding the street names at an intersection or the color of a trafficlight, etc., as well as other information associated with each of these.For example, in some embodiments, feedback may include additionalinformation regarding the amount of currency still needed to complete atransaction, information regarding the identified person, historicalinformation or times and prices of admission etc. of a detected landmarketc. In some embodiments, feedback may include an audible tone, atactile response, and/or information previously recorded by user 100.Feedback-outputting unit 230 may comprise appropriate components foroutputting acoustical and tactile feedback. For example,feedback-outputting unit 230 may comprise audio headphones, a hearingaid type device, a speaker, a bone conduction headphone, interfaces thatprovide tactile cues, vibrotactile stimulators, etc. In someembodiments, processor 210 may communicate signals with an externalfeedback outputting unit 230 via a wireless transceiver 530, a wiredconnection, or some other communication interface. In some embodiments,feedback outputting unit 230 may also include any suitable displaydevice for visually displaying information to user 100.

As shown in FIG. 5A, apparatus 110 includes memory 550. Memory 550 mayinclude one or more sets of instructions accessible to processor 210 toperform the disclosed methods, including instructions for recognizing ahand-related trigger in the image data. In some embodiments memory 550may store image data (e.g., images, videos) captured from theenvironment of user 100. In addition, memory 550 may store informationspecific to user 100, such as image representations of knownindividuals, favorite products, personal items, and calendar orappointment information, etc. In some embodiments, processor 210 maydetermine, for example, which type of image data to store based onavailable storage space in memory 550. In another embodiment, processor210 may extract information from the image data stored in memory 550.

As further shown in FIG. 5A, apparatus 110 includes mobile power source520. The term “mobile power source” includes any device capable ofproviding electrical power, which can be easily carried by hand (e.g.,mobile power source 520 may weigh less than a pound). The mobility ofthe power source enables user 100 to use apparatus 110 in a variety ofsituations. In some embodiments, mobile power source 520 may include oneor more batteries (e.g., nickel-cadmium batteries, nickel-metal hydridebatteries, and lithium-ion batteries) or any other type of electricalpower supply. In other embodiments, mobile power source 520 may berechargeable and contained within a casing that holds apparatus 110. Inyet other embodiments, mobile power source 520 may include one or moreenergy harvesting devices for converting ambient energy into electricalenergy (e.g., portable solar power units, human vibration units, etc.).

Mobile power source 520 may power one or more wireless transceivers(e.g., wireless transceiver 530 in FIG. 5A). The term “wirelesstransceiver” refers to any device configured to exchange transmissionsover an air interface by use of radio frequency, infrared frequency,magnetic field, or electric field. Wireless transceiver 530 may use anyknown standard to transmit and/or receive data (e.g., Wi-Fi, Bluetooth®,Bluetooth Smart, 802.15.4, or ZigBee). In some embodiments, wirelesstransceiver 530 may transmit data (e.g., raw image data, processed imagedata, extracted information) from apparatus 110 to computing device 120and/or server 250. Wireless transceiver 530 may also receive data fromcomputing device 120 and/or server 250. In other embodiments, wirelesstransceiver 530 may transmit data and instructions to an externalfeedback outputting unit 230.

FIG. 5B is a block diagram illustrating the components of apparatus 110according to another example embodiment. In some embodiments, apparatus110 includes a first image sensor 220 a, a second image sensor 220 b, amemory 550, a first processor 210 a, a second processor 210 b, afeedback outputting unit 230, a wireless transceiver 530, a mobile powersource 520, and a power connector 510. In the arrangement shown in FIG.5B, each of the image sensors may provide images in a different imageresolution, or face a different direction. Alternatively, each imagesensor may be associated with a different camera (e.g., a wide anglecamera, a narrow angle camera, an IR camera, etc.). In some embodiments,apparatus 110 can select which image sensor to use based on variousfactors. For example, processor 210 a may determine, based on availablestorage space in memory 550, to capture subsequent images in a certainresolution.

Apparatus 110 may operate in a first processing-mode and in a secondprocessing-mode, such that the first processing-mode may consume lesspower than the second processing-mode. For example, in the firstprocessing-mode, apparatus 110 may capture images and process thecaptured images to make real-time decisions based on an identifyinghand-related trigger, for example. In the second processing-mode,apparatus 110 may extract information from stored images in memory 550and delete images from memory 550. In some embodiments, mobile powersource 520 may provide more than fifteen hours of processing in thefirst processing-mode and about three hours of processing in the secondprocessing-mode. Accordingly, different processing-modes may allowmobile power source 520 to produce sufficient power for poweringapparatus 110 for various time periods (e.g., more than two hours, morethan four hours, more than ten hours, etc.).

In some embodiments, apparatus 110 may use first processor 210 a in thefirst processing-mode when powered by mobile power source 520, andsecond processor 210 b in the second processing-mode when powered byexternal power source 580 that is connectable via power connector 510.In other embodiments, apparatus 110 may determine, based on predefinedconditions, which processors or which processing modes to use. Apparatus110 may operate in the second processing-mode even when apparatus 110 isnot powered by external power source 580. For example, apparatus 110 maydetermine that it should operate in the second processing-mode whenapparatus 110 is not powered by external power source 580, if theavailable storage space in memory 550 for storing new image data islower than a predefined threshold.

Although one wireless transceiver is depicted in FIG. 5B, apparatus 110may include more than one wireless transceiver (e.g., two wirelesstransceivers). In an arrangement with more than one wirelesstransceiver, each of the wireless transceivers may use a differentstandard to transmit and/or receive data. In some embodiments, a firstwireless transceiver may communicate with server 250 or computing device120 using a cellular standard (e.g., LTE or GSM), and a second wirelesstransceiver may communicate with server 250 or computing device 120using a short-range standard (e.g., Wi-Fi or Bluetooth®). In someembodiments, apparatus 110 may use the first wireless transceiver whenthe wearable apparatus is powered by a mobile power source included inthe wearable apparatus, and use the second wireless transceiver when thewearable apparatus is powered by an external power source.

FIG. 5C is a block diagram illustrating the components of apparatus 110according to another example embodiment including computing device 120.In this embodiment, apparatus 110 includes an image sensor 220, a memory550 a, a first processor 210, a feedback-outputting unit 230, a wirelesstransceiver 530 a, a mobile power source 520, and a power connector 510.As further shown in FIG. 5C, computing device 120 includes a processor540, a feedback-outputting unit 545, a memory 550 b, a wirelesstransceiver 530 b, and a display 260. One example of computing device120 is a smartphone or tablet having a dedicated application installedtherein. In other embodiments, computing device 120 may include anyconfiguration such as an on-board automobile computing system, a PC, alaptop, and any other system consistent with the disclosed embodiments.In this example, user 100 may view feedback output in response toidentification of a hand-related trigger on display 260. Additionally,user 100 may view other data (e.g., images, video clips, objectinformation, schedule information, extracted information, etc.) ondisplay 260. In addition, user 100 may communicate with server 250 viacomputing device 120.

In some embodiments, processor 210 and processor 540 are configured toextract information from captured image data. The term “extractinginformation” includes any process by which information associated withobjects, individuals, locations, events, etc., is identified in thecaptured image data by any means known to those of ordinary skill in theart. In some embodiments, apparatus 110 may use the extractedinformation to send feedback or other real-time indications to feedbackoutputting unit 230 or to computing device 120. In some embodiments,processor 210 may identify in the image data the individual standing infront of user 100, and send computing device 120 the name of theindividual and the last time user 100 met the individual. In anotherembodiment, processor 210 may identify in the image data, one or morevisible triggers, including a hand-related trigger, and determinewhether the trigger is associated with a person other than the user ofthe wearable apparatus to selectively determine whether to perform anaction associated with the trigger. One such action may be to provide afeedback to user 100 via feedback-outputting unit 230 provided as partof (or in communication with) apparatus 110 or via a feedback unit 545provided as part of computing device 120. For example,feedback-outputting unit 545 may be in communication with display 260 tocause the display 260 to visibly output information. In someembodiments, processor 210 may identify in the image data a hand-relatedtrigger and send computing device 120 an indication of the trigger.Processor 540 may then process the received trigger information andprovide an output via feedback outputting unit 545 or display 260 basedon the hand-related trigger. In other embodiments, processor 540 maydetermine a hand-related trigger and provide suitable feedback similarto the above, based on image data received from apparatus 110. In someembodiments, processor 540 may provide instructions or otherinformation, such as environmental information to apparatus 110 based onan identified hand-related trigger.

In some embodiments, processor 210 may identify other environmentalinformation in the analyzed images, such as an individual standing infront user 100, and send computing device 120 information related to theanalyzed information such as the name of the individual and the lasttime user 100 met the individual. In a different embodiment, processor540 may extract statistical information from captured image data andforward the statistical information to server 250. For example, certaininformation regarding the types of items a user purchases, or thefrequency a user patronizes a particular merchant, etc. may bedetermined by processor 540. Based on this information, server 250 maysend computing device 120 coupons and discounts associated with theuser's preferences.

When apparatus 110 is connected or wirelessly connected to computingdevice 120, apparatus 110 may transmit at least part of the image datastored in memory 550 a for storage in memory 550 b. In some embodiments,after computing device 120 confirms that transferring the part of imagedata was successful, processor 540 may delete the part of the imagedata. The term “delete” means that the image is marked as ‘deleted’ andother image data may be stored instead of it, but does not necessarilymean that the image data was physically removed from the memory.

As will be appreciated by a person skilled in the art having the benefitof this disclosure, numerous variations and/or modifications may be madeto the disclosed embodiments. Not all components are essential for theoperation of apparatus 110. Any component may be located in anyappropriate apparatus and the components may be rearranged into avariety of configurations while providing the functionality of thedisclosed embodiments. For example, in some embodiments, apparatus 110may include a camera, a processor, and a wireless transceiver forsending data to another device. Therefore, the foregoing configurationsare examples and, regardless of the configurations discussed above,apparatus 110 can capture, store, and/or process images.

Further, the foregoing and following description refers to storingand/or processing images or image data. In the embodiments disclosedherein, the stored and/or processed images or image data may comprise arepresentation of one or more images captured by image sensor 220. Asthe term is used herein, a “representation” of an image (or image data)may include an entire image or a portion of an image. A representationof an image (or image data) may have the same resolution or a lowerresolution as the image (or image data), and/or a representation of animage (or image data) may be altered in some respect (e.g., becompressed, have a lower resolution, have one or more colors that arealtered, etc.).

For example, apparatus 110 may capture an image and store arepresentation of the image that is compressed as a JPG file. As anotherexample, apparatus 110 may capture an image in color, but store ablack-and-white representation of the color image. As yet anotherexample, apparatus 110 may capture an image and store a differentrepresentation of the image (e.g., a portion of the image). For example,apparatus 110 may store a portion of an image that includes a face of aperson who appears in the image, but that does not substantially includethe environment surrounding the person. Similarly, apparatus 110 may,for example, store a portion of an image that includes a product thatappears in the image, but does not substantially include the environmentsurrounding the product. As yet another example, apparatus 110 may storea representation of an image at a reduced resolution (i.e., at aresolution that is of a lower value than that of the captured image).Storing representations of images may allow apparatus 110 to savestorage space in memory 550. Furthermore, processing representations ofimages may allow apparatus 110 to improve processing efficiency and/orhelp to preserve battery life.

In addition to the above, in some embodiments, any one of apparatus 110or computing device 120, via processor 210 or 540, may further processthe captured image data to provide additional functionality to recognizeobjects and/or gestures and/or other information in the captured imagedata. In some embodiments, actions may be taken based on the identifiedobjects, gestures, or other information. In some embodiments, processor210 or 540 may identify in the image data, one or more visible triggers,including a hand-related trigger, and determine whether the trigger isassociated with a person other than the user to determine whether toperform an action associated with the trigger.

Some embodiments of the present disclosure may include an apparatussecurable to an article of clothing of a user. Such an apparatus mayinclude two portions, connectable by a connector. A capturing unit maybe designed to be worn on the outside of a user's clothing, and mayinclude an image sensor for capturing images of a user's environment.The capturing unit may be connected to or connectable to a power unit,which may be configured to house a power source and a processing device.The capturing unit may be a small device including a camera or otherdevice for capturing images. The capturing unit may be designed to beinconspicuous and unobtrusive, and may be configured to communicate witha power unit concealed by a user's clothing. The power unit may includebulkier aspects of the system, such as transceiver antennas, at leastone battery, a processing device, etc. In some embodiments,communication between the capturing unit and the power unit may beprovided by a data cable included in the connector, while in otherembodiments, communication may be wirelessly achieved between thecapturing unit and the power unit. Some embodiments may permitalteration of the orientation of an image sensor of the capture unit,for example to better capture images of interest.

FIG. 6 illustrates an exemplary embodiment of a memory containingsoftware modules consistent with the present disclosure. Included inmemory 550 are orientation identification module 601, orientationadjustment module 602, and motion tracking module 603. Modules 601, 602,603 may contain software instructions for execution by at least oneprocessing device, e.g., processor 210, included with a wearableapparatus. Orientation identification module 601, orientation adjustmentmodule 602, and motion tracking module 603 may cooperate to provideorientation adjustment for a capturing unit incorporated into wirelessapparatus 110.

FIG. 7 illustrates an exemplary capturing unit 710 including anorientation adjustment unit 705. Orientation adjustment unit 705 may beconfigured to permit the adjustment of image sensor 220. As illustratedin FIG. 7, orientation adjustment unit 705 may include an eye-ball typeadjustment mechanism. In alternative embodiments, orientation adjustmentunit 705 may include gimbals, adjustable stalks, pivotable mounts, andany other suitable unit for adjusting an orientation of image sensor220.

Image sensor 220 may be configured to be movable with the head of user100 in such a manner that an aiming direction of image sensor 220substantially coincides with a field of view of user 100. For example,as described above, a camera associated with image sensor 220 may beinstalled within capturing unit 710 at a predetermined angle in aposition facing slightly upwards or downwards, depending on an intendedlocation of capturing unit 710. Accordingly, the set aiming direction ofimage sensor 220 may match the field-of-view of user 100. In someembodiments, processor 210 may change the orientation of image sensor220 using image data provided from image sensor 220. For example,processor 210 may recognize that a user is reading a book and determinethat the aiming direction of image sensor 220 is offset from the text.That is, because the words in the beginning of each line of text are notfully in view, processor 210 may determine that image sensor 220 istilted in the wrong direction. Responsive thereto, processor 210 mayadjust the aiming direction of image sensor 220.

Orientation identification module 601 may be configured to identify anorientation of an image sensor 220 of capturing unit 710. An orientationof an image sensor 220 may be identified, for example, by analysis ofimages captured by image sensor 220 of capturing unit 710, by tilt orattitude sensing devices within capturing unit 710, and by measuring arelative direction of orientation adjustment unit 705 with respect tothe remainder of capturing unit 710.

Orientation adjustment module 602 may be configured to adjust anorientation of image sensor 220 of capturing unit 710. As discussedabove, image sensor 220 may be mounted on an orientation adjustment unit705 configured for movement. Orientation adjustment unit 705 may beconfigured for rotational and/or lateral movement in response tocommands from orientation adjustment module 602. In some embodimentsorientation adjustment unit 705 may be adjust an orientation of imagesensor 220 via motors, electromagnets, permanent magnets, and/or anysuitable combination thereof.

In some embodiments, monitoring module 603 may be provided forcontinuous monitoring. Such continuous monitoring may include tracking amovement of at least a portion of an object included in one or moreimages captured by the image sensor. For example, in one embodiment,apparatus 110 may track an object as long as the object remainssubstantially within the field-of-view of image sensor 220. Inadditional embodiments, monitoring module 603 may engage orientationadjustment module 602 to instruct orientation adjustment unit 705 tocontinually orient image sensor 220 towards an object of interest. Forexample, in one embodiment, monitoring module 603 may cause image sensor220 to adjust an orientation to ensure that a certain designated object,for example, the face of a particular person, remains within thefield-of view of image sensor 220, even as that designated object movesabout. In another embodiment, monitoring module 603 may continuouslymonitor an area of interest included in one or more images captured bythe image sensor. For example, a user may be occupied by a certain task,for example, typing on a laptop, while image sensor 220 remains orientedin a particular direction and continuously monitors a portion of eachimage from a series of images to detect a trigger or other event. Forexample, image sensor 210 may be oriented towards a piece of laboratoryequipment and monitoring module 603 may be configured to monitor astatus light on the laboratory equipment for a change in status, whilethe user's attention is otherwise occupied.

In some embodiments consistent with the present disclosure, capturingunit 710 may include a plurality of image sensors 220. The plurality ofimage sensors 220 may each be configured to capture different imagedata. For example, when a plurality of image sensors 220 are provided,the image sensors 220 may capture images having different resolutions,may capture wider or narrower fields of view, and may have differentlevels of magnification. Image sensors 220 may be provided with varyinglenses to permit these different configurations. In some embodiments, aplurality of image sensors 220 may include image sensors 220 havingdifferent orientations. Thus, each of the plurality of image sensors 220may be pointed in a different direction to capture different images. Thefields of view of image sensors 220 may be overlapping in someembodiments. The plurality of image sensors 220 may each be configuredfor orientation adjustment, for example, by being paired with an imageadjustment unit 705. In some embodiments, monitoring module 603, oranother module associated with memory 550, may be configured toindividually adjust the orientations of the plurality of image sensors220 as well as to turn each of the plurality of image sensors 220 on oroff as may be required. In some embodiments, monitoring an object orperson captured by an image sensor 220 may include tracking movement ofthe object across the fields of view of the plurality of image sensors220.

Embodiments consistent with the present disclosure may includeconnectors configured to connect a capturing unit and a power unit of awearable apparatus. Capturing units consistent with the presentdisclosure may include least one image sensor configured to captureimages of an environment of a user. Power units consistent with thepresent disclosure may be configured to house a power source and/or atleast one processing device. Connectors consistent with the presentdisclosure may be configured to connect the capturing unit and the powerunit, and may be configured to secure the apparatus to an article ofclothing such that the capturing unit is positioned over an outersurface of the article of clothing and the power unit is positionedunder an inner surface of the article of clothing. Exemplary embodimentsof capturing units, connectors, and power units consistent with thedisclosure are discussed in further detail with respect to FIGS. 8-14.

FIG. 8 is a schematic illustration of an embodiment of wearableapparatus 110 securable to an article of clothing consistent with thepresent disclosure. As illustrated in FIG. 8, capturing unit 710 andpower unit 720 may be connected by a connector 730 such that capturingunit 710 is positioned on one side of an article of clothing 750 andpower unit 720 is positioned on the opposite side of the clothing 750.In some embodiments, capturing unit 710 may be positioned over an outersurface of the article of clothing 750 and power unit 720 may be locatedunder an inner surface of the article of clothing 750. The power unit720 may be configured to be placed against the skin of a user.

Capturing unit 710 may include an image sensor 220 and an orientationadjustment unit 705 (as illustrated in FIG. 7). Power unit 720 mayinclude mobile power source 520 and processor 210. Power unit 720 mayfurther include any combination of elements previously discussed thatmay be a part of wearable apparatus 110, including, but not limited to,wireless transceiver 530, feedback outputting unit 230, memory 550, anddata port 570.

Connector 730 may include a clip 715 or other mechanical connectiondesigned to clip or attach capturing unit 710 and power unit 720 to anarticle of clothing 750 as illustrated in FIG. 8. As illustrated, clip715 may connect to each of capturing unit 710 and power unit 720 at aperimeter thereof, and may wrap around an edge of the article ofclothing 750 to affix the capturing unit 710 and power unit 720 inplace. Connector 730 may further include a power cable 760 and a datacable 770. Power cable 760 may be capable of conveying power from mobilepower source 520 to image sensor 220 of capturing unit 710. Power cable760 may also be configured to provide power to any other elements ofcapturing unit 710, e.g., orientation adjustment unit 705. Data cable770 may be capable of conveying captured image data from image sensor220 in capturing unit 710 to processor 800 in the power unit 720. Datacable 770 may be further capable of conveying additional data betweencapturing unit 710 and processor 800, e.g., control instructions fororientation adjustment unit 705.

FIG. 9 is a schematic illustration of a user 100 wearing a wearableapparatus 110 consistent with an embodiment of the present disclosure.As illustrated in FIG. 9, capturing unit 710 is located on an exteriorsurface of the clothing 750 of user 100. Capturing unit 710 is connectedto power unit 720 (not seen in this illustration) via connector 730,which wraps around an edge of clothing 750.

In some embodiments, connector 730 may include a flexible printedcircuit board (PCB). FIG. 10 illustrates an exemplary embodiment whereinconnector 730 includes a flexible printed circuit board 765. Flexibleprinted circuit board 765 may include data connections and powerconnections between capturing unit 710 and power unit 720. Thus, in someembodiments, flexible printed circuit board 765 may serve to replacepower cable 760 and data cable 770. In alternative embodiments, flexibleprinted circuit board 765 may be included in addition to at least one ofpower cable 760 and data cable 770. In various embodiments discussedherein, flexible printed circuit board 765 may be substituted for, orincluded in addition to, power cable 760 and data cable 770.

FIG. 11 is a schematic illustration of another embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure. As illustrated in FIG. 11, connector 730 may becentrally located with respect to capturing unit 710 and power unit 720.Central location of connector 730 may facilitate affixing apparatus 110to clothing 750 through a hole in clothing 750 such as, for example, abutton-hole in an existing article of clothing 750 or a specialty holein an article of clothing 750 designed to accommodate wearable apparatus110.

FIG. 12 is a schematic illustration of still another embodiment ofwearable apparatus 110 securable to an article of clothing. Asillustrated in FIG. 12, connector 730 may include a first magnet 731 anda second magnet 732. First magnet 731 and second magnet 732 may securecapturing unit 710 to power unit 720 with the article of clothingpositioned between first magnet 731 and second magnet 732. Inembodiments including first magnet 731 and second magnet 732, powercable 760 and data cable 770 may also be included. In these embodiments,power cable 760 and data cable 770 may be of any length, and may providea flexible power and data connection between capturing unit 710 andpower unit 720. Embodiments including first magnet 731 and second magnet732 may further include a flexible PCB 765 connection in addition to orinstead of power cable 760 and/or data cable 770. In some embodiments,first magnet 731 or second magnet 732 may be replaced by an objectcomprising a metal material.

FIG. 13 is a schematic illustration of yet another embodiment of awearable apparatus 110 securable to an article of clothing. FIG. 13illustrates an embodiment wherein power and data may be wirelesslytransferred between capturing unit 710 and power unit 720. Asillustrated in FIG. 13, first magnet 731 and second magnet 732 may beprovided as connector 730 to secure capturing unit 710 and power unit720 to an article of clothing 750. Power and/or data may be transferredbetween capturing unit 710 and power unit 720 via any suitable wirelesstechnology, for example, magnetic and/or capacitive coupling, near fieldcommunication technologies, radiofrequency transfer, and any otherwireless technology suitable for transferring data and/or power acrossshort distances.

FIG. 14 illustrates still another embodiment of wearable apparatus 110securable to an article of clothing 750 of a user. As illustrated inFIG. 14, connector 730 may include features designed for a contact fit.For example, capturing unit 710 may include a ring 733 with a hollowcenter having a diameter slightly larger than a disk-shaped protrusion734 located on power unit 720. When pressed together with fabric of anarticle of clothing 750 between them, disk-shaped protrusion 734 may fittightly inside ring 733, securing capturing unit 710 to power unit 720.FIG. 14 illustrates an embodiment that does not include any cabling orother physical connection between capturing unit 710 and power unit 720.In this embodiment, capturing unit 710 and power unit 720 may transferpower and data wirelessly. In alternative embodiments, capturing unit710 and power unit 720 may transfer power and data via at least one ofcable 760, data cable 770, and flexible printed circuit board 765.

FIG. 15 illustrates another aspect of power unit 720 consistent withembodiments described herein. Power unit 720 may be configured to bepositioned directly against the user's skin. To facilitate suchpositioning, power unit 720 may further include at least one surfacecoated with a biocompatible material 740. Biocompatible materials 740may include materials that will not negatively react with the skin ofthe user when worn against the skin for extended periods of time. Suchmaterials may include, for example, silicone, PTFE, kapton, polyimide,titanium, nitinol, platinum, and others. Also as illustrated in FIG. 15,power unit 720 may be sized such that an inner volume of the power unitis substantially filled by mobile power source 520. That is, in someembodiments, the inner volume of power unit 720 may be such that thevolume does not accommodate any additional components except for mobilepower source 520. In some embodiments, mobile power source 520 may takeadvantage of its close proximity to the skin of user's skin. Forexample, mobile power source 520 may use the Peltier effect to producepower and/or charge the power source.

In further embodiments, an apparatus securable to an article of clothingmay further include protective circuitry associated with power source520 housed in in power unit 720. FIG. 16 illustrates an exemplaryembodiment including protective circuitry 775. As illustrated in FIG.16, protective circuitry 775 may be located remotely with respect topower unit 720. In alternative embodiments, protective circuitry 775 mayalso be located in capturing unit 710, on flexible printed circuit board765, or in power unit 720.

Protective circuitry 775 may be configured to protect image sensor 220and/or other elements of capturing unit 710 from potentially dangerouscurrents and/or voltages produced by mobile power source 520. Protectivecircuitry 775 may include passive components such as capacitors,resistors, diodes, inductors, etc., to provide protection to elements ofcapturing unit 710. In some embodiments, protective circuitry 775 mayalso include active components, such as transistors, to provideprotection to elements of capturing unit 710. For example, in someembodiments, protective circuitry 775 may comprise one or more resistorsserving as fuses. Each fuse may comprise a wire or strip that melts(thereby braking a connection between circuitry of image capturing unit710 and circuitry of power unit 720) when current flowing through thefuse exceeds a predetermined limit (e.g., 500 milliamps, 900 milliamps,1 amp, 1.1 amps, 2 amp, 2.1 amps, 3 amps, etc.) Any or all of thepreviously described embodiments may incorporate protective circuitry775.

In some embodiments, the wearable apparatus may transmit data to acomputing device (e.g., a smartphone, tablet, watch, computer, etc.)over one or more networks via any known wireless standard (e.g.,cellular, Wi-Fi, Bluetooth®, etc.), or via near-filed capacitivecoupling, other short range wireless techniques, or via a wiredconnection. Similarly, the wearable apparatus may receive data from thecomputing device over one or more networks via any known wirelessstandard (e.g., cellular, Wi-Fi, Bluetooth®, etc.), or via near-filedcapacitive coupling, other short range wireless techniques, or via awired connection. The data transmitted to the wearable apparatus and/orreceived by the wireless apparatus may include images, portions ofimages, identifiers related to information appearing in analyzed imagesor associated with analyzed audio, or any other data representing imageand/or audio data. For example, an image may be analyzed and anidentifier related to an activity occurring in the image may betransmitted to the computing device (e.g., the “paired device”). In theembodiments described herein, the wearable apparatus may process imagesand/or audio locally (on board the wearable apparatus) and/or remotely(via a computing device). Further, in the embodiments described herein,the wearable apparatus may transmit data related to the analysis ofimages and/or audio to a computing device for further analysis, display,and/or transmission to another device (e.g., a paired device). Further,a paired device may execute one or more applications (apps) to process,display, and/or analyze data (e.g., identifiers, text, images, audio,etc.) received from the wearable apparatus.

Some of the disclosed embodiments may involve systems, devices, methods,and software products for determining at least one keyword. For example,at least one keyword may be determined based on data collected byapparatus 110. At least one search query may be determined based on theat least one keyword. The at least one search query may be transmittedto a search engine.

In some embodiments, at least one keyword may be determined based on atleast one or more images captured by image sensor 220. In some cases,the at least one keyword may be selected from a keywords pool stored inmemory. In some cases, optical character recognition (OCR) may beperformed on at least one image captured by image sensor 220, and the atleast one keyword may be determined based on the OCR result. In somecases, at least one image captured by image sensor 220 may be analyzedto recognize: a person, an object, a location, a scene, and so forth.Further, the at least one keyword may be determined based on therecognized person, object, location, scene, etc. For example, the atleast one keyword may comprise: a person's name, an object's name, aplace's name, a date, a sport team's name, a movie's name, a book'sname, and so forth.

In some embodiments, at least one keyword may be determined based on theuser's behavior. The user's behavior may be determined based on ananalysis of the one or more images captured by image sensor 220. In someembodiments, at least one keyword may be determined based on activitiesof a user and/or other person. The one or more images captured by imagesensor 220 may be analyzed to identify the activities of the user and/orthe other person who appears in one or more images captured by imagesensor 220. In some embodiments, at least one keyword may be determinedbased on at least one or more audio segments captured by apparatus 110.In some embodiments, at least one keyword may be determined based on atleast GPS information associated with the user. In some embodiments, atleast one keyword may be determined based on at least the current timeand/or date.

In some embodiments, at least one search query may be determined basedon at least one keyword. In some cases, the at least one search querymay comprise the at least one keyword. In some cases, the at least onesearch query may comprise the at least one keyword and additionalkeywords provided by the user. In some cases, the at least one searchquery may comprise the at least one keyword and one or more images, suchas images captured by image sensor 220. In some cases, the at least onesearch query may comprise the at least one keyword and one or more audiosegments, such as audio segments captured by apparatus 110.

In some embodiments, the at least one search query may be transmitted toa search engine. In some embodiments, search results provided by thesearch engine in response to the at least one search query may beprovided to the user. In some embodiments, the at least one search querymay be used to access a database.

For example, in one embodiment, the keywords may include a name of atype of food, such as quinoa, or a brand name of a food product; and thesearch will output information related to desirable quantities ofconsumption, facts about the nutritional profile, and so forth. Inanother example, in one embodiment, the keywords may include a name of arestaurant, and the search will output information related to therestaurant, such as a menu, opening hours, reviews, and so forth. Thename of the restaurant may be obtained using OCR on an image of signage,using GPS information, and so forth. In another example, in oneembodiment, the keywords may include a name of a person, and the searchwill provide information from a social network profile of the person.The name of the person may be obtained using OCR on an image of a nametag attached to the person's shirt, using face recognition algorithms,and so forth. In another example, in one embodiment, the keywords mayinclude a name of a book, and the search will output information relatedto the book, such as reviews, sales statistics, information regardingthe author of the book, and so forth. In another example, in oneembodiment, the keywords may include a name of a movie, and the searchwill output information related to the movie, such as reviews, boxoffice statistics, information regarding the cast of the movie, showtimes, and so forth. In another example, in one embodiment, the keywordsmay include a name of a sport team, and the search will outputinformation related to the sport team, such as statistics, latestresults, future schedule, information regarding the players of the sportteam, and so forth. For example, the name of the sport team may beobtained using audio recognition algorithms.

In some embodiments, wearable apparatus 110 may cause a paired device,such as computing device 120, to execute a search query based on one ormore keywords determined from an identified contextual situation.Contextual situations may refer to a combination of circumstances thatmay influence a user's actions, as described below.

In some embodiments, wearable apparatus 110 may be used to control acontrollable device, as described below. For example, the controllabledevice may include a screen in an environment of wearable apparatus 110,and be configured to display search results of a search query that isbased on at least one keyword determined by wearable apparatus 110.Wearable apparatus 110 may cause the controllable device to browsethrough the search results, for example, in response to one or more handgestures detected by wearable apparatus 110.

In some embodiments, wearable apparatus 110 may enter a privacy mode incertain situations, as described below. In some examples, the privacymode may control what keywords and/or search queries are transmittedand/or executed and/or presented. For example, some private searchqueries may be withheld when other persons are in the vicinity of theuser of wearable apparatus 110, when children are in the vicinity of theuser of wearable apparatus 110, and so forth. In some examples, based onwearable apparatus 110 entering a privacy mode, some search queries maybe performed in an incognito or a private browsing mode. In someexamples, based on wearable apparatus 110 entering a privacy mode,keywords may or may not be posted on a social media profile.

Triggering Selected Functions Using Image Analysis

In some embodiments, wearable apparatus 110 may cause a paired device,such as computing device 120, to execute a selected function based oninformation determined from an identified contextual situation.Contextual situations may refer to a combination of circumstances thatmay influence a user's actions. Examples of factors that maydifferentiate contextual situations include: the identity of otherpeople in the vicinity of user 100 (e.g., certain individual, familymembers, coworkers, strangers, and more), the type of activity user 100is engaged (e.g., watching a movie, meeting with an individual, visitinga location, interacting with an object, entering a car, participating ina sport activity, eating a meal, and more), the time in which thesituation took place (e.g., the time of the day, the time of the year,and more), the location in which the situation occurs (e.g., home,working place, shopping mall, and more).

User 100 may encounter a significant number of diverse types ofcontextual situations during the course of a given day. Identifying andtransmitting information about all the contextual situations that user100 encounters throughout his/her day may drain the battery power ofwearable apparatus 110. To address this drawback, wearable apparatus 110may determine and transmit information only when encountering contextualsituations that user 100 is interested in. However, determining whichcontextual situations are of interest to user 100 may be complex. Forexample, one user may want to know about the food he/she ate, anotheruser may want to know about people he/she met, and yet another user maywant to track activities that he/she participated in. Therefore,wearable apparatus 110 may enable user 100 and/or paired devices todynamically indicate the contextual situations he/she is interested in,and computing device 120 may execute a selected function after user 100encounters the selected contextual situation. In another example, at onetime and/or after detecting a particular context, a paired device (e.g.,computing device 120) may execute one or more applications that useinformation related to one set of contextual situations, while executingapplications that use information related to a second set of contextualsituations at another time and/or after detecting a particular context.In another example, a first paired device may execute one or moreapplications that use information related to one set of contextualsituations, while a second paired device may execute one or moreapplications that use information related to a second set of contextualsituations, and wearable apparatus 110 may transmit differentinformation to different paired devices according to the contextualsituations.

One way for wearable apparatus 110 to determine which contextualsituations are of interest to user 100 is using category tags. Categorytags may include digital data that characterizes contextual situations.The characterization of a contextual situation may be general (e.g., atype of activity, a general location, a typo of product, etc.) or may bemore specific (e.g., a name of a person, an address, a name of aproduct, etc.).

One or more category tags may be assigned to an image based on thecontent identified in the image and/or based on metadata information(e.g., location, time) associated with the image. One skilled in the artwill appreciate that any image associated with at least one contextualsituation may include a plurality of category tags.

In one embodiment, wearable apparatus 110 may receive a request fromcomputing device 120 to transmit information associated with aparticular category tag. The request may include a specific combinationof circumstances and/or factors that defines the looked-for contextualsituation associated with that category tag. For example, user 100 maywant to keep records of his encounters with certain individuals outsideworking hours. In some embodiments, the request may be provided by anapplication executing on a device remote from wearable apparatus 110(e.g., a smartphone, tablet, or smart watch paired with wearable device110, or a server in communication with wearable device 110). Forexample, user 100 may make use of an application or have registered withan application to keep track of his encounters with other people. Aspart of configuring user preferences with such an application, user 100may have specified an interest, in particular, of tracking encounterswith co-workers outside of working hours. The configuring may involve,for example, selecting and/or enabling functionality to consider imagesthat have been classified (e.g., via category tags) related toco-workers (e.g., certain identified individuals) and a particular timeperiod (e.g., outside of typical working hours).

In operation, wearable apparatus 110 may associate a category tag withat least one image obtained from image sensor 220. With regards to theexample above, an image captured after 6 p.m. that includes at least oneof the identified individuals may be associated with a category tagsuch, as for example, “Hanging out with my coworkers.” The category tagmay also be associated with a selected function that a paired device mayexecute. The selected function associated with a category tag may be tostore information (e.g., when, where, how long, who was there) about thedetected contextual situation. With regards to the example above,wearable apparatus 110 may process images with the category tag “Hangingout with my coworkers” to determine information about the encounter andto transmit the information to computing device 120 to cause computingdevice 120 to store the information in memory 550 b and/or in a remoteserver. Computing device 102 and/or the remote server may then processthe received information and provide feedback to user 100 (e.g., asummary of co-workers that user 100 encounter after 6 p.m. Additionalexemplary embodiments of contextual situations, category tags, and thetypes of selected functions that may be triggered are discussed infurther detail with respect to FIGS. 17A-17D.

FIG. 17A is a schematic illustration of a contextual situation that maytrigger computing device 120 to execute one or more selected functionsconsistent with the present disclosure. The contextual situationillustrated in this figure is a person present in an area in front ofuser 100. Wearable apparatus 110 may identify this contextual situationby analyzing one or more images, such as image 1710. After identifyingthis contextual situation, wearable apparatus 110 may cause computingdevice 120 to execute the one or more selected functions.

In one embodiment, the processing device may receive a request fromcomputing device 120 to transmit image-related information associatedwith the category tag. The requested image-related information mayinclude an image of the person, and the selected function may includeproviding information about the person included in the image to user100. For example, assuming user 100 is registered to an online datingservice and previously selected that he/she is interested in blondwomen. The dating application may communicate with wearable apparatus110 and request facial images of any unknown blond women encountered byuser 100.

As shown, the category tags 1700 that may be assigned to image 1710 mayinclude a “new people” tag, a “coffee shop” tag, and a “dating app” tag.In this case, the “new people” tag was assigned to image 1710 becausewearable apparatus 110 does not recognize the woman in front of user100; the “coffee shop” tag was assigned to image 1710 based on, forexample, information from a Global Positioning System (GPS); and the“dating app” tag was assigned to image 1710 because the woman in frontof user 100 is blond. Thereafter, wearable apparatus 110 may transmit animage including the woman's face to computing device 120. The datingapplication installed on the user's smartphone may search for the imageand find that the image is of a woman who is registered to a datingservice. Computing device 120 may then send the woman's profile page1715 to user 100.

Although the above example relates to identifying a person for purposesof retrieving a dating profile, the “new people” tag may be used byother applications. For example, applications requesting images taggedwith a “new people” tag may include those requesting images ofunidentified persons to check whether a person included in an image maybe a missing person or a person who is wanted by law enforcement.Accordingly, computing device 120 may provide an alert related to amissing-persons status of the person, or an alert related to awanted-by-law-enforcement status of the person. Further, in someembodiments, wearable apparatus 110 may be used by law enforcementofficers to receive real-time indications about wanted suspects inpresent in their surroundings on a paired device (e.g., an alert as to awanted status or that an encountered individual has a criminal record).

FIG. 17B is a schematic illustration of another contextual situationthat may trigger computing device 120 to execute one or more selectedfunctions consistent with the present disclosure. The contextualsituation illustrated in this figure is an object present in an area infront of user 100. Wearable apparatus 110 may identify this contextualsituation by analyzing one or more images, such as image 1720. Afteridentifying this contextual situation, wearable apparatus 110 may causecomputing device 120 to execute the one or more selected functions.

In one embodiment, computing device 120 may request image-relatedinformation that includes at least one detail about the object. Forexample, the object may be a business card and the at least one detailmay include contact information. In the contextual situation illustratedin FIG. 17B, user 100 is registered to a business-oriented socialnetworking service and had installed an application of this service onhis/her smartphone. The installed application may communicate withwearable apparatus 110 and request contact information of any businesscard that user 100 holds that is not his/her own. As shown, the categorytags 1700 that may be assigned to image 1720 may include an “objects”tag, a “business card” tag, and a “business connection website” tag. Inthis case, as “the business connection website” tag was assigned toimage 1720 because the business card that user 100 holds is other thanhis/her own, wearable apparatus 110 may transmit the contact informationof John Doe listed on the business card to computing device 120 toexecute a selected function. In one embodiment, the selected functionmay include storing at least one detail from the business card. The atleast one detail may be obtained by, for example, executing an opticalcharacter recognition (OCR) function. Computing device 120 may thenstore the contact information of John Doe in memory 550 b. In anotherembodiment, the selected function may include opening a website andautomatically including at least one detail from the business card in,for example, a profile (e.g., a social networking site profile) of user100. For example, after receiving the contact information of John Doe,computing device 120 may enable user 100 to connect with John Doe via abusiness connection website.

In other embodiments, computing device 120 may request other detailssuch as a product type or a product name. For example, user 100 may walkin a store and look at a product, and wearable apparatus 110 may beprogrammed to transmit the product type or the product name to computingdevice 120. For example, wearable apparatus 110 may provide an image ofthe product and/or wearable apparatus 110 may execute an OCR function toidentify the product name from a label included in an image. In thisexample, the selected function may include opening a website thatcompares prices, which may automatically search the product name toenable user 100 to review the prices of the same product in otherstores.

FIG. 17C is a schematic illustration of yet another contextual situationthat may trigger computing device 120 to execute one or more selectedfunctions consistent with the present disclosure. The contextualsituation illustrated in this figure is the presence of food in front ofuser 100. Wearable apparatus 110 may identify this contextual situationby analyzing one or more images, such as image 1730. After identifyingthis contextual situation, wearable apparatus 110 may cause computingdevice 120 to execute one or more selected functions.

In the contextual situation illustrated in FIG. 17C, computing device120 may request image-related information that includes an indicationthat user 100 is engaging with food associated with a dietaryrestriction. In one embodiment, the selected function may includeupdating a database with information related to the food associated withthe dietary restriction. In another embodiment, the selected functionmay include providing an alert 1735 regarding the food in front of user100. For example, the requested image-related information may include anindication that user 100 is engaging with food associated with a dietaryrestriction, such as a food allergy. In this example, user 100 may havepreviously installed an application on a paired device that maintains alist of known ingredients that user 100 is allergic to. The installedapplication may communicate with wearable apparatus 110 and requestimages of any packaged food that user 100 is engaged with. As shown, thecategory tags 1700 that may be assigned to image 1730 may include a“food” tag, a “lunch” tag, and an “allergy check” tag. When processor210 detects that user 100 is engaging with the packaged food, wearableapparatus 110 may transmit an image of the packaged food to computingdevice 120. The installed application may search an offline database oran online database to determine if the packaged food that user 100 isengaging with includes an ingredient that may be dangerous to user 100and provide a real-time warning to user 100. In other embodiments, therequested image-related information may include an indication that user100 is engaging with food associated with a dietary restriction, wherethe dietary restriction relates to a disease (e.g., diabetes) or aweight loss goal.

FIG. 17D is a schematic illustration of still another contextualsituation that may trigger computing device 120 to execute one or moreselected functions consistent with the present disclosure. Thecontextual situation illustrated in this figure is performing ahealth-related activity. Wearable apparatus 110 may identify thiscontextual situation by analyzing a one or more images, such as image1740. After identifying this contextual situation, wearable apparatus110 may cause computing device 120 to execute one or more selectedfunctions.

In the contextual situation illustrated in FIG. 17D, computing device120 may request image-related information that includes an indicationthat user 100 is engaged in the health-related activity. In oneembodiment, the selected function may include providing information toan individual related to user 100. The individual related to user 100may be the user's physician or a family member. For example, theinformation provided to the individual related to user 100 may include amedicine reminder concerning the user and an alert related to a healthstatus of user 100. In another embodiment, the selected function mayinclude providing information associated with the health-relatedactivity to user 100. The health-related activity may include: takingmedicine, performing a physical exercise, receiving a treatment, andmore. In the example illustrated in FIG. 17D, user 100 installed anapplication on a paired device that monitors whether he/she takes allhis/her medicine on time. The installed application may communicate withwearable apparatus 110 and request a notification if user 100 takesmedicine. As shown, the category tags 1700 that may be assigned to image1740 may include a “drinking water” tag and “health monitor” tag. Whenprocessor 210 detects that user 100 is engaging with the health-relatedactivity, wearable apparatus 110 may transmit an indication to computingdevice 120. The indication may include an image of the medicine. Theinstalled application may check that user 100 is about to take thecorrect medicine as admitted and provide a real-time feedback 1745 touser 100.

A person skilled in the art can appreciate that many more types ofcontextual situations (not shown in the figures) may trigger computingdevice 120 to execute one or more selected functions consistent with thepresent disclosure. In addition, while specific selected functions weredescribed with regards to certain types of contextual situations, thepresent disclosure is not limited to the disclosed examples. Asdescribed below, any combination of selected functions may be triggeredin response to any contextual situations, and any number of separatedoperations may be included in a selected function.

Furthermore, in some embodiments, for example, analyzing one or moreimages captured by wearable apparatus 110 may involve edgeidentification, in which an image is analyzed to detect pixels at whichdiscontinuities (e.g., sudden changes in image brightness) occur andedges (e.g., edges of the external object) are identified to coincidewith the detected pixels. Alternatively or additionally, in someembodiments analyzing one or more images may involve identifying inand/or extracting from an image pixels representative of objects in theenvironment, such as the external object. Pixels may be determined to berepresentative of an external object based on, for example, other imagesof the external device or similar external devices maintained, e.g., ina database and/or predetermined data describing the external objectmaintained, e.g., in a database. Alternatively or additionally, pixelsmay be determined to be representative of an external object based on,for example, a trained neural network configured to detect predeterminedexternal objects. Other types of analysis are possible as well,including, but not limited to, gradient matching, greyscale matching,scale-invariant feature transform (SIFT) matching, and/or interpretationtrees.

FIG. 18A is a message flow diagram that depicts the communicationbetween wearable apparatus 110 and computing device 120 for threedifferent types of selected functions. The first type of selectedfunctions involves computing device 120. Specifically, computing device120 may send wearable apparatus 110 a request 1800 that defines acontextual situation. After wearable apparatus 110 detects thecontextual situation, it may send image-related information 1810 tocomputing device 120. Examples for the first type of selected functioninclude: storing data on memory 550 b (e.g., counting the occurrences ofa certain event) and providing alerts to user 100 (e.g., as illustratedin FIGS. 29A-29D). The second type of selected functions involvescomputing device 120 communicating with server 250. Specifically, aftercomputing device 120 receives image-related information 1810, it maytransmit information 1820 to server 250. Information 1820 may beidentical to image-related information 1810 or data derived fromimage-related information 1810. Examples of the second type of selectedfunction include: updating a database (e.g., updating a cloud-based lifelogging service), forwarding a message to an individual (e.g., informinga parent that a child is engaging in a dangerous activity), opening awebsite (e.g., opening a weather forecast website when user 100 looks athis/her umbrella). The third type of selected functions involvesretrieving information from server 250. Specifically, after computingdevice 120 receives image-related information 1810, it may transmit aninquiry 1830 to server 250, and receive back information 1840 to bepresented to user 100 via computing device 120. Examples for this typeof selected function include: providing information about a person or anobject in the environment of user 100 (e.g., as illustrated in FIG. 17Aand FIG. 17C).

FIG. 18B is a schematic illustration of three different types ofscenarios where wearable apparatus 110 may trigger multiple selectedfunctions. In the first scenario, wearable apparatus 110 may receivemultiple requests from a paired device (e.g., computing device 120).Each of the requests may be associated with a different contextualsituation and trigger a different selected function. Specifically, aprocessing device (e.g., processor 210) may receive a first request1800A and a second request 1800B from computing device 120 to transmitimage-related information associated with first and second categorytags. The first category tag is associated with a first selectedfunction and the second category tag is associated with a secondselected function. Thereafter, the processing device may analyzeobtained images to detect first and second contextual situations. Basedon the detected first and second contextual situations, the processingdevice may associate at least one image with the first category tag andat least one other image with the second category tag. The processingdevice may also determine first image-related information 1810Aassociated with the detected first contextual situation and the firstcategory tag, and second image-related information 1810B associated withthe detected second contextual situation and the second category tag.The processing device may further cause a transmitter (e.g., transceiver530) to transmit first image-related information 1810A and secondimage-related information 1810B to computing device 120 to causecomputing device 120 to execute the first and second selected functionsbased on the determined image-related information. For example, thefirst contextual situation may be a person present in an area in frontof user 100 (as illustrated in FIG. 17A), and the second contextualsituation may be the presence of food present in front of user 100 (asillustrated in FIG. 17C).

In the second scenario, wearable apparatus 110 may receive multiplerequests from multiple paired devices. Each of the requests may beassociated with a different contextual situation and may trigger adifferent selected function. Specifically, the processing device mayreceive first request 1800A from first computing device 120A and secondrequest 1800B from second computing device 120B. Thereafter, theprocessing device may analyze obtained images to detect the first andthe second contextual situations. Based on the detected first and secondcontextual situations, the processing device may associate at least oneimage with the first category tag and at least one other image with thesecond category tag. The processing device may also determine firstimage-related information 1810A associated with the first contextualsituation and the first category tag, and second image-relatedinformation 1810B associated with the second contextual situation andthe second category tag. The processing device may further cause atransmitter (e.g., transceiver 530) to transmit first image-relatedinformation 1810A to first computing device 120A and to transmit secondimage-related information 1810B to second computing device 120B, andthereby cause first computing device 120A to execute the first selectedfunction based on first image-related information 1810A, and secondcomputing device 120B to execute the second selected function based onsecond image-related information 1810B. For example, the first computingdevice 120A may be the user's smartphone and the second computing device120B may be the user's smartwatch. Accordingly, a warning that user 100is engaging with food including a dangerous ingredient may be providedto the user's smartwatch, and information about a person in front ofuser 100 may be provided to the user's smartphone.

In the third scenario, wearable apparatus 110 may receive multiplerequests from multiple paired devices. The requests may be associatedwith the same contextual situation, but may trigger different selectedfunctions. Specifically, the processing device may receive first request1800A from first computing device 120A and second request 1800B fromsecond computing device 120B. Thereafter, the processing device mayanalyze obtained images to detect a contextual situation. Based on thedetected contextual situation, the processing device may associate atleast one image with the first category tag and the second category tag.The processing device may also determine first image-related information1810A associated with the detected contextual situation and the firstcategory tag, and second image-related information 1810B associated withthe detected contextual situation and the second category tag. Theprocessing device may further cause the transmitter to transmit firstimage-related information 1810A to first computing device 120A andsecond image-related information 1810B to second computing device 120B,and thereby cause first computing device 120A to execute the firstselected function based on first image-related information 1810A, andsecond computing device 120B to execute the second selected functionbased on second image-related information 1810B. For example, if thecontextual situation includes the presence of food including aningredient that may be dangerous, the user's smartwatch may provide areal-time warning to user 100, while the user's smartphone may provideinformation to an individual related to user 100. In some embodiments,wearable apparatus 110 may cause the first selected function and thesecond selected function to be executed concurrently or subsequently.The term “concurrently” means that the two selected functions occurduring coincident or overlapping time periods, either where one beginsand ends during the duration of the other, or where a later one startsbefore the completion of the other. In another scenario (not illustratedin the figure) a single paired device may execute concurrently orsubsequently multiple selected functions based on a single contextualsituation.

FIG. 19 is a flowchart showing an exemplary process 1900 for causingcomputing device 120 to execute a selected function based on informationdetermined from an identified contextual situation, consistent withdisclosed embodiments. Wearable apparatus 110 may implement process 1900to trigger one or more selected functions based on different contextualsituations, for example, as illustrated in FIGS. 17A-17D.

As illustrated in FIG. 19, at step 1910, a processing device (e.g.,processor 210) may receive a request from computing device 120 totransmit information associated with a category tag. At step 1920, theprocessing device may obtain at least one image and at step 1930 theprocessing device may analyze the at least one image to detect acontextual situation associated with the at least one image. At step1940, processing device may determine image-related informationassociated with the detected contextual situation. And at step 1950, theprocessing device may cause the paired device to execute the selectedfunction based on the determined image-related information. These stepsof process 1900 are discussed in greater detail below.

Specifically, at step 1910, the processing device may receive a requestfrom computing device 120 to transmit information associated with acategory tag. The request may be generated by user 100 interacting withan application running on computing device 120 or generated by computingdevice 120 without a direct interaction of user 100. In someembodiments, the category tag may be associated with a selectedfunction. In a first example, when the selected function includesproviding information related to the person included in the at least oneimage to user 100, the category tag may include a people category. Inthis example, the requested information includes at least part of the atleast one image. In a second example, when the selected functionincludes storing information related to at least one detail about anobjected located in front of user 100, the category tag may include anobject category. In this example, the requested information may includethe at least one detail about an object included in the at least oneimage. In a third example, when the selected function includes updatinga database based on information related to the food associated with thedietary restriction, the category tag may include a food category. Inthis example, the requested information may include an indication thatuser 100 is engaging with food associated with a dietary restriction. Ina fourth example, when the selected function includes providinginformation to an individual related to user 100, the category tag mayinclude a health-related category. In this example, the requestedinformation may include an indication that user 100 is engaging in ahealth-related activity.

At step 1920, the processing device may obtain at least one image, andat step 1930 the processing device may analyze the at least one image todetect a contextual situation associated with the at least one image. Insome embodiments, the processing device may be programmed to detected inthe at least one image a contextual situation that includes a presenceof an object of interest. For example, the presence of a known person,the presence of text or a logo, the presence of recognized object, orthe presence of an indicator of an event type. The event type mayinclude at least one of: a sporting event, a family event, awork-related event, driving, reading, eating, and socializing. In otherembodiments, the processing device may be programmed to receive audioinformation and to detect the contextual situation based on the receivedaudio information and the obtained at least one image. The audioinformation may include a voice command, a name of a known individual,recognizable background noises, and more. In other embodiments,processing device may be programmed to retrieve additional informationfrom one or more sources that may assist in identifying or improving thecertainty level in the identification of the contextual situation. Inone example, the processing device may access location information fromthe GPS to better detect a contextual situation. Alternatively, theprocessing device may derive location information from other sources,such as available Wi-Fi networks' service set identifier (SSID).

At step 1940, the processing device may determine image-relatedinformation associated with the detected contextual situation. Consistedwith the present disclosure, computing device 120 may define in therequest the type of image-related information that wearable apparatus110 should determine and transmit after detecting the contextualsituation. Alternatively, the type of image-related information may beselected as the result of default settings. In some embodiments, asmentioned above, the requested information may include at least part ofthe at least one image. Specifically, some selected functions mayinvolve procedures, such as Optical Character Recognition (OCR) and/orfacial recognition. Since these procedures may not require all of theimages captured by image sensor 220, determining the image-relatedinformation may include identifying a Region of Interest (ROI) andenabling transmission of the ROI. In the example depicted in FIG. 17A,the determined image-related information may include a cropped imagethat includes a face of the woman in front user 100. In otherembodiments, also as mentioned above, the requested information mayinclude an indication that user 100 is engaging with a person and/orwith an object. The indication may be a one-bit message, processed dataincluding metadata information (e.g., time, location), and/or at least aportion of the captured image data that includes a representation of thecontextual situation. As illustrated in the third scenario of FIG. 18B,in some cases different types of image-related information may bedetermined in response to detecting a single contextual situation. Thedifferent types may correspond with different selected functions.

At step 1950, after determining the image-related information, theprocessing device may cause computing device 120 to execute the selectedfunction based on the determined image-related information. The selectedfunction may be executed in real-time or at a later time (e.g., whencomputing device is being charged). One type of selected function mayinclude providing information to user 100. Another type of selectedfunction may include storing information in a memory. In one embodiment,even when the selected function is providing an alert to user 100 (suchas illustrated in FIGS. 29A-29D), computing device 120 may be programedto store information about the provided alert in memory 550 b or in aremote server. In another embodiment, the selected function may includeinitiating a search related to the determined image-related informationin a database. In this case, the selected function may include causinginformation related to a result of the search to be displayed on adisplay associated with computing device 120. In addition, computingdevice 120 may determine that the received image-related information isnot sufficient to execute the selected function, and requests fromwearable apparatus 110 for additional information. In the exampledepicted in FIG. 17A, computing device 120 may receive an image thatincludes the face of the woman in front user 100, but the image was notgood enough for the facial recognition search engine. Accordingly,computing device 120 can request from wearable apparatus 110 to transmitanother image. As those who are skilled in the art will appreciate, atleast some of the steps depicted in FIG. 19 can be performedsimultaneously or in a different order than that shown in the figure.

Visual Pairing of External Devices with a Wearable Apparatus

In some embodiments, an external device may be recognized in image datacaptured by wearable apparatus 110. An external device may be any devicein an environment of wearable apparatus 110 that is configured to pairwith wearable apparatus 110. Example external devices may include, butare not limited to computing devices, personal electronic devices,mobile devices, desktop devices, entertainment devices, householddevices, audio and/or visual devices, illumination devices, appliances,fixtures, thermostats, televisions, coffee makers, printers, lights,lamps, Wi-Fi support devices, network devices, etc.

In some cases, the external device may provide one or more controllablefunctions. A controllable function may be any changeable aspect of theexternal device. Example controllable functions may include, but are notlimited to, mode (e.g., a power-saving mode, a color printing mode, ashuffle mode, etc.), brightness, intensity, volume, position, on/offstate, stop/play state, station and/or channel selection (e.g., for anaudio and/or visual device), temperature, and/or speed.

In some cases, it may be desirable for wearable apparatus 110 todetermine if the external device is one with which wearable apparatus110 may pair (e.g., with which a communication path may be established).Pairing may permit wearable apparatus 110, for example, to control acontrollable function of the external device or simply to exchangeinformation with the external device. In order to determine whether theexternal device is one with which wearable apparatus 110 may pair,wearable apparatus 110 may transmit a signal configured to cause aresponse by the external device, such as a change in at least one aspectof the external device. Such a signal may be referred to as aninterrogation signal. The interrogation signal may be transmitted bywearable apparatus 110 itself and/or through one or more intermediatedevices, such as a device paired with wearable apparatus 110.

FIG. 20 is a block diagram illustrating an example of the components ofwearable apparatus 110 for visually pairing with an external device. Asshown in FIG. 20, wearable apparatus 110 includes image sensor 220,wireless transceiver 530, memory 550, and processor 210. While only oneimage sensor 220, wireless transceiver 530, memory 550, and processor210 are shown, it will be understood that more of any of thesecomponents may be included. Further, while these components are shown tobe included in wearable apparatus 110, in other embodiments one or moreof these components may be remote from and configured to communicatewith wearable apparatus 110 (e.g., distributed over one or more serversin communication with wearable device 110 over a network). In otherembodiments, wearable apparatus 110 may include other components, suchas any of the components described above in connection with FIGS. 5A-5C.

Image sensor 220 may take any of the forms described above in connectionwith FIGS. 2, 3A, 4A-4B, 5A-5C, and 7. Similarly, wireless transceiver530 may take any of the forms described above in connection with FIGS.5A-5C. Memory 550 may likewise take any of the forms described above inconnection with FIGS. 5A-5C (including memory 550 a and 550 b), andprocessor 210 may take any of the forms described above in connectionwith FIGS. 2 and 5A-5C.

Image sensor 220 may be any device configured to capture images and/or astream of images from an environment of a user of the wearable apparatus110. The environment may include, for example, one or more externaldevices. The images and/or stream of images may include, for example,real-time image data of a field-of-view of the user. As discussed above,image sensor 220 may be configured to detect and convert optical signalsinto electrical signals, and the electrical signals may be used to forman image or a video stream (i.e., the stream of images) based on thedetected signal.

Memory 550 may contain software modules consistent with the presentdisclosure. As shown, included in memory 550 are an external devicedetection module 2002, an interrogation signal module 2004, and acontrol signal module 2006. Modules 2002, 2004, and 2006 may containsoftware instructions for execution by processor 210, as describedbelow. External device detection module 2002, interrogation signalmodule 2004, and control signal module 2006 may cooperate to facilitatevisual pairing of wireless apparatus 110 with the external device.

Processor 210 may be configured to receive the stream of images fromimage sensor 220 and to analyze the stream of images to detect theexternal device in the environment of the user. In some embodiments,processor 210 may be configured to execute software instructions inexternal device detection module 2002 to receive the stream of imagesfrom image sensor 220 and analyze the stream of images to detect theexternal device in the environment. As described above, processor 210may be configured to extract information from the stream of images.Extracting information, as described above, includes any process bywhich information associated with objects, individuals, locations,events, etc., is identified in the stream of images by any means knownto those of ordinary skill in the art. Processor 210 may be configuredto identify information associated with the external device in thestream of images.

Analyzing the stream of images may involve any analysis by which theexternal device may be detected based on the stream of images. In someembodiments, for example, analyzing the stream of images may involveedge identification, in which an image is analyzed to detect pixels atwhich discontinuities (e.g., sudden changes in image brightness) occurand edges (e.g., edges of the external object) are identified tocoincide with the detected pixels. Alternatively or additionally, insome embodiments analyzing the stream of images may involve identifyingin and/or extracting from an image pixels representative of one or moreobjects in the environment, such as the external object. Pixels may bedetermined to be representative of an external object based on, forexample, other images of the external device or similar external devicesmaintained, e.g., in a database and/or predetermined data describing theexternal object maintained, e.g., in a database. Alternatively oradditionally, pixels may be determined to be representative of anexternal object based on, for example, a trained neural networkconfigured to detect predetermined external objects. Other types ofanalysis are possible as well, including, but not limited to, gradientmatching, greyscale matching, scale-invariant feature transform (SIFT)matching, and/or interpretation trees.

Processor 210 may be further configured to cause wireless transceiver530 to transmit an interrogational signal to the external device, andwireless transceiver 530 may be configured to transmit the interrogationsignal. In some embodiments, processor 210 may be configured to executesoftware instructions in interrogation signal module 2004 to causewireless transceiver 530 to transmit the interrogation signal. Theinterrogation signal may be any signal configured to cause a change inat least one aspect of the external device. The aspect may be, forexample, a feature of the external device's appearance or anothervisually recognizable and/or detectable attribute. For example, theinterrogation signal may be configured to cause the external device toilluminate and/or blink a light and/or display on the external device,modify a position of the external device and/or some component of theexternal device, and/or display certain information.

The interrogation signal may take the form of, for example, a radiofrequency (RF) signal (e.g., a radio frequency identification (RFID)signal), a Bluetooth signal, an optical signal (e.g., an infraredsignal, a visible light signal), and/or a Wi-Fi signal (e.g., an IEEE802.11 signal). In some embodiments, different interrogation signals maybe used for different external devices and/or different types ofexternal devices. In some embodiments, interrogation signal module 2004may determine which interrogation signal to use based on the detectedexternal device. Alternatively or additionally, interrogation signalmodule 2004 may attempt more than one interrogation signal (e.g., in apredetermined order, an order dependent on the detected external device,etc.).

Wireless transceiver 530 may take different forms for differentinterrogation signals. For example, wireless transceiver 530 may takethe form of a radio frequency transmitter and/or transceiver, aBluetooth radio, and/or an optical transmitter (e.g., an LED). Where theexternal device is configured to pair with wearable apparatus 110, theexternal device may include a component configured to receive theinterrogation signal, such as a radio transceiver, a Bluetooth detector,and/or an optical receiver (e.g., a photo diode detector). In someembodiments, the external device may include more than one component forreceiving more than one type of interrogation signal (e.g., an externaldevice may be configured to receive both an optical and a Bluetoothinterrogation signal).

As described above, the interrogation signal may be configured to causea change in at least one aspect of the external device. In someembodiments, the interrogation signal may include instructions (e.g., acommand) to cause the external device to change the aspect(s) of theexternal device. For example, the interrogation signal may command theexternal device to illuminate, pulse, and/or blink a light and/ordisplay on the external device, change an intensity of a light emittedby the external device, modify a position of the external device or somecomponent of the external device, and/or display certain information.

In some embodiments, the external device may include one or morecomponents for carrying out the command included in the interrogationsignal. For example, where the interrogation signal commands theexternal device to illuminate a light (e.g., an LED array) on theexternal device, the external device may include a light configured tobe illuminated or deilluminated. As another example, where theinterrogation signal commands the external device to modify a positionof the external device, the external device may include one or moremotion mechanisms, such as an actuator, electric motor, wheels, orgears, and so forth, which may enable the external device to modify itsposition. As still another example, where the interrogation signalcommands the external device to modify a position of some component ofthe external device, the external device may include, in addition to oneor more motion mechanisms, a component adapted for movement, such as anarm. In some embodiments, the components configured to carrying out thecommand included in the interrogation signal may be components speciallyadapted to carry out the command. Alternatively, the components may haveother purposes in the external device. For example, where theinterrogation signal commands the external device to display certaininformation, the external device may display the information on adisplay of the external device that additionally serves to display otherinformation at the external device.

In some embodiments, the change in the aspect(s) of the external devicemay be between binary states on the external device. For example, theexternal device may change a light from an on state to an off state. Asanother example, the external device may change a position of theexternal device from a resting position to a ready position.Alternatively, in some embodiments the interrogation signal may specifyan extent of change in the aspect(s) of the external device. Forexample, the interrogation signal may specify that the external deviceis to move a component of the external device (e.g., raise an arm) by aspecified distance and/or in a specified direction. As another example,the interrogation signal may specify that the external device is tochange an intensity of a light by a specified amount and/or in aspecified direction (e.g., reduce an intensity of the light by aspecified amount). As another example, the interrogation signal mayspecify that the external device is to change the color of an emittedlight. The change in the aspect(s) may be temporary. For example, theinterrogation signal may specify that the external device is to modify adisplay to display certain information for a specified period of time.As another example, the interrogation signal may specify that theexternal device is to blink a light for a specified period of time.

Processor 210 may be further configured to analyze the stream of imagesto detect the change in the at least one aspect of the external device,as caused by the interrogation signal. In some embodiments, processor210 may be configured to execute software instructions in externaldevice detection module 2002 to detect the change in the at least oneaspect of the external device. For example, where the interrogationsignal caused the external device to illuminate and/or blink a light ordisplay on the external device, processor 210 may be configured toextract information from the stream of images indicating theillumination and/or blinking. As another example, where theinterrogation signal caused the external device to modify a position ofthe external device or some component of the external device, processor210 may be configured to extract information from the stream of imagesindicating the changed position. As still another example, where theinterrogation signal caused the external device to display certaininformation, processor 210 may be configured to extract information fromthe stream of images indicating the displayed information.

Processor 210 may be further configured to store in memory 550information relating to the external device after detection of thechange in the at least one aspect of the external device. Theinformation may include, for example, information relating to a locationassociated with the external device, a type associated with the externaldevice, information related to the appearance of the external device, apicture of the external device, and/or an indication of at least onecontrollable function associated with the external device. In anotherexample, the external device may be a wearable external device, andprocessor 210 may be further configured to identify the person wearingthe wearable external device and/or to determine information related tothe person wearing the wearable external device, such as the person'sgender, height, information related to the appearance of the person, apicture of the person, a picture of the face of the person, and soforth.

In some embodiments, a location of the external device may be determinedby, for example, querying the external device (e.g., where the externaldevice includes a global positioning system or is otherwise configuredto determine its own location). Alternatively or additionally, alocation of the external device may be determined relative to wearableapparatus 110 and/or another reference in the environment using one ormore of stereo image analysis, time-of-flight analysis, triangulation,structured light techniques, and/or modulated light techniques using,for example, radar, lidar, and/or infrared detection. In someembodiments, wearable apparatus 110 may include and/or may pair withanother device that includes a component configured for determining oneor both of a geographic position of wearable apparatus 110, a geographicposition of the external device, and/or a relative position of theexternal device to wearable apparatus 110, such as a global positioningsystem and/or a radar or lidar transceiver. In some embodiments,wearable apparatus 110 and/or another device paired with wearableapparatus 110 may be configured to generate a map or otherrepresentation of the environment (e.g., using simultaneous location andmapping (SLAM) techniques) indicating a relative location of theexternal device to wearable apparatus 110 and/or one or more otherobjects in the environment. The map may be, for example, a grid map or atopological map.

In some embodiments, wearable apparatus 110 and the external device maybe paired, such that wearable apparatus 110 may, for example, exchangeinformation with the external device or control a controllable functionof the external device. Processor 210 may execute software instructionsin control signal module 2006 to cause a control signal to betransmitted to the external device. The control signal may be configuredto control one or more controllable functions of the external device, asfurther described below.

FIG. 21 is a flowchart illustrating an exemplary process 2100 forvisually pairing with an external device. Process 2100 is described withreference to FIGS. 22A-22C, which are schematic illustrations of anexemplary pairing between wearable apparatus 110 and an external device2202 in an environment 2200. One of ordinary skill in the art willrecognize that FIGS. 22A-22C are examples, and other kinds of devicesmay be paired with wearable apparatus 110.

As shown, process 2100 may begin at step 2102 with receiving a stream ofimages. The stream of images may be received from, for example, at leastone image sensor (e.g., image sensor 220) of the wearable apparatus 110,which may be configured to capture the stream of images from anenvironment of a user of the wearable apparatus 110.

Example environment 2200 is shown in FIG. 22A. As shown, environment2200 includes a user 100 wearing a wearable apparatus 110. Whilewearable apparatus 110 is shown to be configured to connect on anexterior surface of clothing of the user 100, similar to wearableapparatus 110 described in connection with FIG. 9, above, it will beunderstood that wearable apparatus 110 may take any of the formsdescribed herein, including but not limited to those shown in FIGS.1A-D, 3A-B, 4A-B, and 8-16. Wearable apparatus 110 may include and/or becommunicatively coupled to an image sensor, such as image sensor 220described above, that is configured to capture a stream of images fromenvironment 2200. While environment 2200 is shown as an indoorenvironment, it will be understood that environment 2200 may be anyproximity surrounding user 100, whether indoor, outdoor, or both.

Returning to FIG. 21, process 2100 may continue at step 2104 withanalyzing the stream of images to detect an external device. Forexample, at least one processing device (e.g., processor 210) in thewearable apparatus 110 may be programmed to receive the stream of imagesfrom the image sensor 220 and analyze the stream of images to detect theexternal device in the environment of the user.

An example external device 2202 is shown in FIG. 22A. As shown, externaldevice 2202 may be a light source, such as a visible light source or aninfrared light source. While the external device 2202 is shown to be alight source, external device 2202 may take other forms as well. Forexample, rather than being a light source, external device 2202 mayinclude, be included in, be communicatively coupled to, and/or otherwiseassociated with a light source. As another example, external device 2202may be, include, be included in, be communicatively coupled to, and/orotherwise associated with a computing device (e.g., a desktop computer,laptop computer, tablet computer, printer, database, server, etc.), apersonal electronic device (e.g., mobile device, cellular device, table,smartphone, smart watch, e-reader device, etc.), an entertainment device(e.g., television, digital media player, music player, radio, etc.), ahousehold device (e.g., refrigerator, oven, stove, microwave, alarmsystem, appliance, fixture, etc.), audio and/or visual devices (e.g.,display, projector, speaker, etc.), an illumination device (e.g., lamp,etc.), an appliance (e.g., washing machine, dryer, blender, etc.), afixture (e.g., ceiling fan, lock, safe, garage door, etc.), and/or awearable device.

The stream of images from environment 2200 captured by the imagesensor(s) 220 of wearable apparatus 110 may include a representation ofexternal device 2202, such that a processor 210 included in and/orcommunicatively coupled to wearable apparatus 110 may analyze the streamof images to detect external device 2202 in environment 2200 of user100.

Returning to FIG. 21, process 2100 may continue at step 2106 withtransmitting an interrogation signal. For example, the processingdevice(s) in the wearable apparatus 110 may be programmed to cause atleast one transmitter (e.g., wireless transceiver 530) in the wearableapparatus 110 to transmit the interrogation signal. The interrogationsignal may be configured to cause a change in at least one aspect of theexternal device.

An example interrogation signal 2204 is illustrated in FIG. 22A. Asshown, wearable apparatus 110 may transmit (e.g., using a wirelesstransceiver 530 included in and/or communicatively coupled to wearableapparatus 110) interrogation signal 2204 to external device 2202. In theevent external device 2202 is a device with which wearable apparatus 110cannot pair, interrogation signal 2204 may have no effect on externaldevice 2202. However, in the event external device 2202 is a device withwhich wearable apparatus 110 can pair, interrogation signal 2204 maycause a change in at least one aspect of external device 2202.

An example change 2206 in at least one aspect of external device 2202 isshown in FIG. 22B. External device 2202 may exhibit change 2206 inresponse to interrogation signal 2204. As shown, change 2206 mayinclude, for example, a change in illumination of external device 2202,such as external device 2202 blinking. Other example changes 2206 mayinclude turning off, turning on, dimming, and/or brightening. In someembodiments, for example where external device 2202 is not, does notinclude, is not included in and/or is not associated with a lightsource, change 2206 be another alteration of a feature of an appearanceand/or other visually recognizable or detectable attribute of externaldevice 2202. For example, external device 2202 may illuminate and/orblink a display on external device 2202, modify a position of externaldevice 2202 or some component of external device 2202, and/or displaycertain information. The change 2206 may be permanent or may betemporary.

Returning to FIG. 21, process 2100 may continue at step 2108 withanalyzing the stream of images to detect the change. For example, theprocessing device may be programmed to analyze the stream of images todetect the change in the at least one aspect of the external device. Asshown in FIG. 22B, the stream of images from environment 2200 capturedby the image sensor(s) 220 of wearable apparatus 110 may include arepresentation of external device 2202, including change 2206, such thatthe processor 210 included in and/or communicatively coupled to wearableapparatus 110 may analyze the stream of images to detect external device2202 and change 2206.

Returning to FIG. 21, process 2100 may continue at step 2110 withstoring information relating to the external device. For example, theprocessing device may be programmed to, after detection of the change inthe at least one aspect of the external device, store in a memory (e.g.,memory 550) of the wearable apparatus 110 information relating to theexternal device.

The information may include, for example, a location associated with theexternal device, such as a geographic location of the external deviceand/or a location of the external device relative to one or more of thewearable apparatus 110, the user, and/or another object in theenvironment. Alternatively or additionally, the information may includea type associated with the external device. The type may, for example,describe the external device. For example, the type may indicate a typeof the external device (e.g., a light source, a microwave, or a garageopener), a brand, style, a version, and/or a design of the externaldevice (e.g., a brand of television, a style of ceiling fan, a design ofspeaker, etc.), an identifier that uniquely identifies the externaldevice (e.g., a MAC address, an IP address, etc.), an identifier of aparticular type or brand of the external device, or other feature of theexternal device. Still alternatively or additionally, the informationmay include a picture of the external device. Still alternatively oradditionally, the information may include an indication of at least onecontrollable function associated with the external device. Theindication of the controllable function may, for example, describe thefunction that can be controlled, such as a mode (e.g., a power-savingmode, a color printing mode, a shuffle mode, etc.), brightness,intensity, volume, position, state, on/off state, stop/play state,station or selection (e.g., for an audio and/or visual device),temperature, and/or speed. Alternatively or additionally, the indicationof the controllable function may, for example, describe how the functioncan be controlled, such as options (e.g., among modes, among, positions,among stations, etc.) and levels (e.g., of volume, brightness, etc.).Still alternatively or additionally, the information may includeinformation related to devices coupled with the external device. Forexample, the external device may comprise a set-top box or a gameconsole, the coupled device may comprise a TV connected to the externaldevice, and the information related to the TV may comprise a location, abrand, a version, size, a picture of the TV, and so forth. In anotherexample, the external device may comprise a thermostat, the coupleddevice may comprise an HVAC unit connected to the thermostat, and theinformation related to the HVAC unit may comprise a location, a brand, aversion, a state, a picture of the HVAC unit, and so forth. Stillalternatively or additionally, the external device may be a wearableexternal device, and the information may comprise information related toa person wearing the wearable external device, such as the person'sgender, height, information related to the appearance of the person, apicture of the person, a picture of the face of the person, and soforth.

Example information relating to external device 2202 in FIG. 22B mayinclude a location of external device 2202, such as a geographiclocation of external device 2202 and/or a location of external device2202 relative to one or more of wearable apparatus 110, user 100, and/oranother object in the environment (e.g., relative to the table shown).Alternatively or additionally, example information relating to externaldevice 2202 may include a type associated with external device 2202,such as a type of external device 2202 (e.g., a light source), a brandof external device 2202 (e.g., a brand of the light source), a style ofexternal device 2202 (e.g., whether the light source emits visible orinfrared light, what type and/or how many light bulbs are used in thelight source, a type of shade included in the light source, whether thelight source is movable or fixed, etc.), a design of external device2202 (e.g., whether the light source can be dimmed, whether the lightsource operates on a timer, etc.), and/or other feature of externaldevice 2202. Still alternatively or additionally, the information mayinclude an indication of at least one controllable function associatedwith external device 2202, such as a mode (e.g., the information mayindicate that the light source can be turned on and/or off, that thelight source can operate in a power-saving mode, that the light sourcecan operate on a timer, etc.), a brightness and/or intensity of thelight source, a position of the light source (if the light source ismovable), or another function of the light source. Alternatively oradditionally, the indication of the controllable function may, forexample, describe how the function of external device 2202 can becontrolled, such as options (e.g., among modes or positions of the lightsource, etc.) and levels (e.g., of brightness, intensity, etc.).

In some embodiments, the wearable apparatus 110 may detect a gesture orgestures by the user and, in response, may cause at least one aspect ofthe external device to be controlled, for example using the informationrelating to the external device stored in memory. For example, theprocessing device may be further programmed to analyze the stream ofimages to detect at least one recognized gesture (e.g., a hand-relatedtrigger, as described above) made by the user. As shown in FIG. 22C, forexample, user 100 may make a recognized gesture 2208.

While the recognized gesture 2208 is shown to be a raise of an arm ofuser 100, it will be understood that other recognized gestures arepossible as well.

Based on the detected recognized gesture(s), the processing device maycause a control signal to be transmitted (e.g., by wireless transceiver530) to the external device. The control signal may be configured tocontrol at least one aspect of the external device that is associatedwith the recognized gesture. In some embodiments, the control signal maybe configured to cause the external device to activate (e.g., turn on),deactivate (e.g., turn off), change setting (e.g., changing the settingsof an HVAC system, changing the illumination brightness, changing thevolume of a sound system, and so forth). As shown in FIG. 22C, forexample, based on the recognized gesture 2208, wearable apparatus 110may transmit control signal 2210 to external device 2202, therebycontrolling at least one aspect 2212 of external device 2202. Aspect2212 may be associated with recognized gesture 2208.

As shown, aspect 2212 may be an illumination of external source 2202,and control signal 2210 may be configured to control the illumination ofexternal source 2202 by, for example, turning on, turning off, dimming,or brightening the illumination. Alternatively or additionally,controlling aspect 2212 may involve controlling any feature of anappearance or other visually recognizable or detectable attribute ofexternal device 2202, including modifying a position of external device2202 or some component of external device 2202 and/or displaying certaininformation. The control may be permanent or may be temporary.Alternatively or additionally, controlling aspect 2212 may involvecontrolling any of the controllable functions described above, such as amode of external device 2202, a brightness and/or intensity of externaldevice 2202, a position of external device 2202, or another function ofthe light source.

While certain environments, external devices, changes, recognizedgestures, and aspects are shown in FIGS. 22A-22C, it will be understoodthat these are merely exemplary and that other environments, externaldevices, changes, recognized gestures, and aspects are possible as well.

In some embodiments, step 2104 may detect in the stream of images one ormore external devices in the environment of the user, and step 2106 maydetect one or more external devices able to receive an interrogationsignal, for example, through a wireless communication protocol. Thenumber of external devices detected by step 2104 may be smaller, larger,or equal to the number of external devices detected by step 2106. Insome examples, the visual appearance in the stream of images of theexternal devices detected by step 2104 may hint at which externaldevices detected by step 2104 correlate to which external devicesdetected by step 2106. In some examples, process 2100 may go through oneor more of the external devices detected by step 2106, transmitinterrogation signals to the external devices, and step 2108 may analyzethe stream of images to detect the change corresponding to thetransmitted interrogation signals, differentiating among the detectedexternal devices based on the detected change. For example, thetransmitted interrogation signals may be transmitted at different times,and step 2108 may differentiate among the detected external devicesbased on the timing of the detected change. For example, the transmittedinterrogation signals may be configured to cause different changes, andstep 2108 may differentiate among the detected external devices based onthe type of the detected change. For example, step 2106 may detect, forexample through a wireless communication protocol, two external devicesable to receive an interrogation signal, and step 2104 may detect fourexternal devices. Step 2106 may transmit interrogation signals to thetwo external devices detected by step 2106, and step 2108 may identifywhich, if any, of the four external devices detected by step 2104,corresponds to the external devices detected by step 2106, based on adetected change in the appearance of the external device detected bystep 2104. In another example, step 2106 may detect, for example,through a wireless communication protocol, two wearable devices and onelight source, and based on the visual appearance in the stream of imagesof the external devices, step 2104 may detect four external devices andrecognize one as a wearable device and three external devices as lightsources. Step 2106 may transmit interrogation signals to the twowearable devices detected by step 2106, and step 2108 may identifywhich, if any, of the two wearable devices detected by step 2106, is thewearable device detected by step 2104, based on a detected change in theappearance of the wearable device detected by step 2104. Step 2106 maytransmit interrogation signals to the light source detected by step2106, and step 2108 may identify which, if any, of the three lightsources detected by step 2104, is the light source detected by step2106, based on a detected change in the appearance, or lack thereof, ofthe light sources detected by step 2104.

Controlling an External Device Using a Wearable Apparatus

In some embodiments, wearable apparatus 110 may be used to control acontrollable device. A controllable device may be any device in anenvironment of wearable apparatus 110 that can be controlled by wearableapparatus 110. Example controllable devices may include, but are notlimited to, computing devices, personal electronic devices, mobiledevices, desktop devices, entertainment devices, household devices,audio and/or visual devices, illumination devices, appliances, fixtures,thermostats, televisions, coffee makers, printers, light sources, lamps,Wi-Fi support devices, network devices, wearable devices, etc.

In some cases, wearable apparatus 110 may control (e.g., change) one ormore aspects of the controllable device. Example controllable aspectsmay include, but are not limited to, mode (e.g., a power-saving mode, acolor printing mode, a shuffle mode, etc.), brightness, intensity,volume, position, on/off state, locked/unlocked state, armed/unarmedstate, stop/play state, station or channel selection (e.g., for an audioand/or visual device), temperature, time, and/or speed. As an example,the controllable device may be or may include a light source, andwearable apparatus 110 may cause the controllable device to change anillumination state of the light source. As another example, thecontrollable device may be or may include a heating device, ventilationdevice, air conditioning device, and/or HVAC system, and wearableapparatus 110 may be configured to change one or more settings (e.g., atemperature or fan speed) of the heating device, ventilation device, airconditioning device, and/or HVAC system. As still another example, thecontrollable device may be or may include a locking mechanism, andwearable apparatus 101 may be configured to cause the controllabledevice to lock or unlock the locking mechanism.

In some cases, wearable apparatus 110 may control the aspect(s) of thecontrollable device in response to detecting a visual trigger. A visualtrigger may be any trigger that is visually detectable by wearableapparatus 110, such as a hand or body gesture by a user of wearableapparatus 110 and/or movement of an object associated with the user,such as a stylus or glove.

In order to control a controllable device, wearable apparatus 110 may beconfigured to identify the controllable device and detect the visualtrigger by analyzing images obtained by wearable apparatus 110. Based onthe detection of the trigger, wearable apparatus 110 may transmit asignal configured to change the aspect(s) of the controllable device.The signal may be referred to as a command. The command may betransmitted to the controllable device by wearable apparatus 110 itselfand/or through one or more intermediate devices, such as a device pairedwith wearable apparatus 110.

FIG. 23 is a block diagram illustrating an example of the components ofa wearable apparatus 110 for controlling a controllable device. As shownin FIG. 23, wearable apparatus 110 may include image sensor 220,wireless transceiver 530, memory 550, and processor 210. While only oneimage sensor 220, wireless transceiver 530, memory 550, and processor210 are shown, it will be understood that more of any of thesecomponents may be included. Further, while these components are shown tobe included in wearable apparatus 110, in other embodiments one or moreof these components may be remote from and configured to communicatewith wearable apparatus 110 (e.g., distributed over one or more serversin communication with wearable device 110 over a network). In otherembodiments, wearable apparatus 110 may include other components, suchas any of the components described above in connection with FIGS. 5A-5C.

Image sensor 220 may take any of the forms described above in connectionwith FIGS. 2, 3A, 4A-4B, 5A-5C, and 7. Similarly, wireless transceiver530 may take any of the forms described above in connection with FIGS.5A-5C. Memory 550 may likewise take any of the forms described above inconnection with FIGS. 5A-5C (including memory 550 a, 550 b), andprocessor 210 may take any of the forms described above in connectionwith FIGS. 2 and 5A-5C.

Image sensor 220 may be any device (e.g., camera, CCD, etc.) configuredto obtain images from an environment of a user of the wearable apparatus110. The environment may include, for example, one or more controllabledevices. The images may include, for example, real-time image data of afield-of-view of the user. As discussed above, image sensor 220 may beconfigured to detect and convert optical signals into electricalsignals, and the electrical signals may be used to form an image or avideo stream (i.e., the stream of images) based on the detected signal.

Memory 550 may contain software modules consistent with the presentdisclosure. As shown, included in memory 550 are a controllable deviceidentification module 2302, a visual trigger detection module 2304, anda command module 2306.

Modules 2302, 2304, and 2306 may contain software instructions forexecution by processor 210, as described below. Controllable deviceidentification module 2302, visual trigger detection module 2304, andcommand module 2306 may cooperate to facilitate controlling of acontrollable device be wearable apparatus 110.

Processor 210 may be configured to receive the images obtained by imagesensor 220 and to analyze the images to identify the controllable devicein the environment of the user. In some embodiments, processor 210 maybe configured to execute software instructions in controllable deviceidentification module 2302 to obtain the images from image sensor 220and analyze the images to identify the controllable device in theenvironment. As described above, processor 210 may be configured toextract information from the stream of images. Extracting information,as described above, includes any process by which information associatedwith objects, individuals, locations, events, etc., is identified in thestream of images by any means known to those of ordinary skill in theart. Processor 210 may be configured to identify information associatedwith the controllable device in the images.

Processor 210 may be further configured to analyze the images to detecta visual trigger associated with the controllable device. In someembodiments, processor 210 may be configured to execute softwareinstructions in visual trigger detection module 2304 to analyze theimages to detect the visual trigger. As described above, processor 210may be configured to extract information from the stream of images.Extracting information, as described above, includes any process bywhich information associated with objects, individuals, locations,events, etc., is identified in the stream of images by any means knownto those of ordinary skill in the art. Processor 210 may be configuredto identify information associated with the visual trigger in theimages.

Analyzing the stream of images to identify the controllable deviceand/or detect the visual trigger may involve any analysis by which thecontrollable device and/or visual trigger may be identified or detectedbased on the images. In some embodiments, for example, analyzing theimages may involve edge identification, in which an image is analyzed todetect pixels at which discontinuities (e.g., sudden changes in imagebrightness) occur and edges (e.g., edges of the controllable object, abody part of the user, and/or an object associated with the user) areidentified to coincide with the detected pixels. Alternatively oradditionally, in some embodiments analyzing the images may involveidentifying in and/or extracting from an image pixels representative ofone or more objects in the environment, such as the controllable object,a body part of the user, and/or an object associated with the user.Pixels may be determined to be representative of an object based on, forexample, other images of the object maintained, e.g., in a databaseand/or predetermined data describing the object maintained, e.g., in adatabase (e.g., other images of the controllable device, of the bodypart of the user, and/or of the device associated with the user).Alternatively or additionally, pixels may be determined to berepresentative of an object based on, for example, a trained neuralnetwork configured to detect predetermined objects (e.g., predeterminedcontrollable devices, body parts of the user, and/or devices associatedwith the user). Other types of analysis are possible as well, including,but not limited to, gradient matching, greyscale matching,scale-invariant feature transform (SIFT) matching, and/or interpretationtrees.

Processor 210 may be further configured to, based on the detection ofthe visual trigger, cause wireless transceiver 530 to transmit a commandto the controllable device. In some embodiments, processor 210 may beconfigured to execute software instructions in command module 2306 tocause wireless transceiver 530 to transmit the command. The command maybe configured to change at least one aspect of the controllable device.The aspect may be, for example, a feature of the controllable device'sappearance or function. Example aspects may include, but are not limitedto, mode (e.g., a power-saving mode, a color printing mode, a shufflemode, etc.), brightness, intensity, volume, position, on/off state,locked/unlocked state, armed/unarmed state, stop/play state, station orchannel selection (e.g., for an audio and/or visual device),temperature, time, and/or speed.

The command may take the form of, for example, a radio frequency (RF)signal (e.g., a radio frequency identification (RFID) signal), aBluetooth signal, an optical signal (e.g., an infrared signal, a visiblelight signal), or a Wi-Fi signal (e.g., an IEEE 802.11 signal). In someembodiments, different commands may be used for different controllabledevices and/or different types of controllable devices. In someembodiments, control module 2304 may determine which command to usebased on the identified controllable device. Alternatively oradditionally, control module 2304 may attempt more than one command(e.g., in a predetermined order, an order dependent on the identifiedcontrollable device, etc.).

Wireless transceiver 530 may take different forms for differentcommands. For example, wireless transceiver 530 may take the form of aradio frequency transmitter or transceiver, a Bluetooth radio, and/or anoptical transmitter (e.g., an LED). Likewise, the controllable devicemay include a component configured to receive the command, such as aradio transceiver, a Bluetooth detector, and/or an optical receiver(e.g., a photo diode detector). In some embodiments, the controllabledevice may include more than one component for receiving more than onetype of command (e.g., a controllable device may be configured toreceive both an optical and a Bluetooth command).

As described above, the command may be configured to cause a change inat least one aspect of the controllable device. In some embodiments, thecommand may include instructions to cause the controllable device tochange the aspect(s) of the controllable device. For example, thecommand may cause the controllable device to change a mode in which thecontrollable device is operating (e.g., a power-saving mode, a colorprinting mode, a shuffle mode, etc.) or change a brightness, intensity,volume, position, on/off state, stop/play state, station or channelselection (e.g., for an audio and/or visual device), temperature, and/orspeed of the controllable device.

In some embodiments, the controllable device may include one or morecomponents for carrying out the command. For example, where the wearableapparatus 110 commands the controllable device to change a brightness ofthe controllable device, the controllable device may include a lightconfigured to be brightened or dimmed. As another example, where thewearable apparatus 110 commands the controllable device to change froman unlocked state to a locked state, the controllable device may includea locking mechanism that enables the controllable device to change to alocked state. In some embodiments, the components configured to carryingout the command may be components specially adapted to carry out thecommand. Alternatively, the components may have other purposes in thecontrollable device. For example, where the command commands thecontrollable device to display certain information, the controllabledevice may display the information on a display of the controllabledevice that additionally serves to display other information at thecontrollable device.

In some embodiments, the change in the aspect(s) of the controllabledevice may be between binary states on the controllable device. Forexample, the controllable device may change a light from an on state toan off state. As another example, the controllable device may changefrom a power-saving mode to a non-power-saving mode. Alternatively, insome embodiments the command may specify an extent of change in theaspect(s) of the controllable device. For example, the command mayspecify a change in brightness and/or color of a light or may specifythat the controllable device is to change a time for performing afunction (e.g., a cook time on a microwave) by a specified amount and/orin a specified direction (e.g., add 30 seconds to the cook time). Thechange in the aspect(s) may be temporary. For example, the command mayspecify that the controllable device is to modify a display device todisplay certain information for a specified period of time. As anotherexample, the command may specify that the controllable device is toremain in a power-saving mode for a specified period of time.

FIG. 24 is a flowchart illustrating an exemplary process 2400 forcontrolling a controllable device. Process 2400 is described withreference to FIGS. 25A-C, which are schematic illustrations of anexemplary controlling of a controllable device 2502 by a wearableapparatus 110 in an environment 2500. One of ordinary skill in the artwill recognize that FIGS. 25A-C are examples, and other kinds of devicesmay be paired with wearable apparatus 110.

As shown, process 2400 may begin at step 2402 with obtaining images. Theimages may be obtained from, for example, at least one image sensor(e.g., image sensor 220) of the wearable apparatus 110, which may beconfigured to capture the images from an environment of a user of thewearable apparatus 110.

Example environment 2500 is shown in FIG. 25A. As shown, environment2500 includes a user 100 wearing a wearable apparatus 110. Whilewearable apparatus 110 is shown to be configured to connect on anexterior surface of clothing of the user 100, similar to wearableapparatus 110 described in connection with FIG. 9, above, it will beunderstood that wearable apparatus 110 may take any of the formsdescribed herein, including but not limited to those shown in FIGS.1A-D, 3A-B, 4A-B, and 8-16. Wearable apparatus 110 may include and/or becommunicatively coupled to an image sensor, such as image sensor 220described above, that is configured to capture images from environment2500. While environment 2500 is shown as an indoor environment, it willbe understood that environment 2500 may be any proximity surroundinguser 100, whether indoor, outdoor, or both.

Returning to FIG. 24, process 2400 may continue at step 2404 withanalyzing the images to identify a controllable device. For example, atleast one processing device (e.g., processor 210) in the wearableapparatus 110 may be programmed to analyze the images to identify thecontrollable device in the environment of the user of the wearableapparatus 110.

In some embodiments, in addition to identifying the controllable device,wearable apparatus 110 (e.g., at least one processing device in thewearable apparatus 110) may further analyze the images to identify acurrent state of the controllable device. The state may be any currentsetting or level of an aspect of the controllable device. The state maybe, for example, a binary state of the controllable device, such ason/off state, a locked/unlocked state, an armed/unarmed state, astop/play state, etc. Alternatively or additionally, the state may be avariable state of the controllable device, such as a brightness,intensity, volume, position, station or channel selection (e.g., for anaudio and/or visual device), temperature, time, and/or speed of thecontrollable device. Still alternatively or additionally, the state maybe a mode (e.g., a power-saving mode, a color printing mode, a shufflemode, etc.) of the controllable device.

Alternatively or additionally, in some embodiments, in addition toidentifying the controllable device, wearable apparatus 110 (e.g., atleast one processing device in the wearable apparatus 110) may furtheranalyze the images to identify a context associated with thecontrollable device. The context may be any features of the environmentin which the controllable device is identified. The context may be, forexample, a context associated with a particular user behavior, such aswaking up, going to sleep, arriving, leaving, cleaning, preparing ameal, eating a meal, hosting a party, exercising, an object held by theuser, etc. Alternatively or additionally, the context may be a contextassociated with a particular environmental feature, such as a time ofday, an indoor or outdoor temperature, a state of another controllabledevice, an action involving the controllable device, etc.

An example controllable device 2502 is shown in FIG. 25A. As shown,controllable device 2502 may include a security system. While only akeypad of the security system/alarm is shown, controllable device 2502may be the keypad itself and/or a security alarm system (e.g.,comprising sensors, alerting devices, locks, etc.) of which the keypadis a part. Moreover, while the controllable device 2502 is shown to be asecurity alarm, controllable device 2502 may take other forms as well.For example, rather than being a security alarm, controllable device2502 may include, be included in, be communicatively coupled to, and/orotherwise associated with a security alarm. As another example,controllable device 2502 may be, include, be included in, becommunicatively coupled to, and/or otherwise associated with a computingdevice (e.g., a desktop computer, laptop computer, tablet computer,printer, database, server, etc.), personal electronic device (e.g.,mobile device, cellular device, table, smartphone, smart watch, e-readerdevice, toy, etc.), entertainment device (e.g., television, digitalmedia player, music player, radio, etc.), household device (e.g.,refrigerator, oven, stove, microwave, alarm system, appliance, fixture,heating device, ventilation device, air conditioning device, HVACsystem, security system, etc.), audio and/or visual devices (e.g.,display, projector, speaker, etc.), illumination device (e.g., lamp,etc.), appliance (e.g., washing machine, dryer, blender, etc.), vehicle(e.g., automobile, lawn equipment, bicycle, boat, etc.), wearabledevice, and/or fixture (e.g., ceiling fan, lock, safe, door, garagedoor, etc.).

The images from environment 2500 captured by the image sensor(s) 220 ofwearable apparatus 110 may include a representation of controllabledevice 2502, such that a processor 210 included in and/orcommunicatively coupled to wearable apparatus 110 may analyze the imagesto identify controllable device 2502 in environment 2500 of user 100. Insome embodiments, wearable apparatus 110 may further determine a currentstate of the controllable device 2502, as described above. For example,wearable apparatus 110 may determine that controllable device 2502 is inan armed state, at a certain brightness level, and/or a door associatedwith controllable device 2502 is unlocked. Alternatively oradditionally, in some embodiments wearable apparatus 110 may furtherdetermine a context associated with the controllable device 2502, asdescribed above. For example, wearable apparatus 110 may determine acontext associated with an action involving controllable device 2502, anobject held by user 100, and/or a state of controllable device 2502.

In some embodiments, instead or in addition to step 2404, process 2400may also comprise a procedure for identifying controllable devices inthe vicinity of wearable apparatus 110 by detecting signals transmittedby the controllable devices. For example, wireless transceiver 530 maydetect wireless signals transmitted by controllable devices in thevicinity of wearable apparatus 110. In another example, a RFID readerand/or RFID scanner may detect RFID enabled devices in the vicinity ofwearable apparatus 110. In some examples, instead or in addition to step2404, process 2400 may also determine the current state of thecontrollable device by receiving information related to the currentstate in signals transmitted by the controllable devices.

In some embodiments, in process 2400, step 2404 may be executed before,after or simultaneously with step 2606. In some embodiments, in process2400, step 2404 and/or step 2606 may be executed after or simultaneouslywith step 2402.

In some embodiments, wearable apparatus 110 may pair (e.g., establish acommunication path) with the controllable device, as described above inconnection with FIGS. 20-22A-C. Pairing may permit wearable apparatus110 to, for example, transmit command signals to control thecontrollable device, as described below, or simply exchange informationwith the controllable device. In some embodiments, for example, wearableapparatus 110 may transmit an interrogation signal configured to cause achange in at least one aspect of the controllable device, analyze one ormore images captured by the image sensor after transmission of theinterrogation signal to detect the change in the aspect(s) of thecontrollable device and, after detecting the change, store in the memoryinformation relating to the controllable device. The informationrelating to the controllable device may include, for example, a locationassociated with the controllable device, a type associated with thecontrollable device, and/or at least one controllable functionassociated with the controllable device. The interrogation signal, theaspect(s) of the controllable device, and/or the information may takeany of the forms described above in connection with FIGS. 20-22A-C.

In some embodiments, as described above in connection with FIGS.20-22A-C, wearable apparatus 110 may additionally be configured toanalyze one or more images captured by the image sensor to detect atleast one recognized gesture made by the user and transmit a controlsignal configured to control at least one aspect of the controllabledevice. The aspect(s) of the controllable device may be associated withthe recognized gesture, as described above. The control signal and theaspect(s) of the controllable device may take any of the forms describedabove in connection with FIGS. 20-22A-C.

Returning to FIG. 24, process 2400 may continue at step 2406 withanalyzing the images to detect a visual trigger. For example, at leastone processing device (e.g., processor 210) in the wearable apparatus110 may be programmed to analyze the images to detect a visual triggerassociated with the controllable device. Example visual triggers mayinclude, but are not limited to, movements of the user 100, a body partof the user 100, and/or an object associated with the user 100 such as astylus or glove. Alternatively or additionally, the visual trigger mayinclude a hand gesture, an action involving a hand of the user 100,and/or an action involving the controllable device. In some embodiments,wearable apparatus 110 may store in memory 550 associations betweenvisual triggers and controllable devices. Such associations may bepredetermined, user-defined, and/or provided by the controllable device(e.g., during pairing with the controllable device, as described above).In some embodiments, wearable device 110 may further identify a typeassociated with the visual trigger. For example, wearable device 110 mayidentify if the visual trigger is a hand gesture, an action involvingthe user's hand, an action involving the controllable device, etc.

An example visual trigger 2506 is illustrated in FIG. 25B. While thevisual trigger 2506 is shown to be a raise of an arm of user 100, itwill be understood that other visual triggers are possible as well. Forexample, the visual trigger 2506 may be another movement by the sameand/or another body part of the user 100, such as a hand gesture (e.g.,an upward flick of an index finger). As another example, the visualtrigger 2506 may be a movement by an object associated with the user100, such as a stylus held by the user 100 or a glove worn by the user100. As still another example, the visual trigger 2506 may be an actioninvolving the controllable device 2502, such as tapping the controllabledevice 2502 or making a movement near the controllable device 2502.

The images from environment 2500 captured by the image sensor(s) 220 ofwearable apparatus 110 may include a representation of the visualtrigger 2506, such that a processor 210 included in and/orcommunicatively coupled to wearable apparatus 110 may analyze the imagesto detect the visual trigger 2506. In embodiments where wearableapparatus 110 determines a context associated with the controllabledevice 2502, as described above, the context may alternatively oradditionally be associated with the visual trigger.

In some embodiments, instead or in addition to step 2406, process 2400may also comprise a procedure for detecting a non-visual trigger, suchas a non-visual trigger produced by user 100. Some examples of suchnon-visual triggers may include a press of a button, an audible trigger,and so forth. In some embodiments, instead or in addition to step 2404,process 2400 may also determine a type associated with the non-visualtrigger. For example, when the non-visual trigger comprises a press of abutton, the type of the non-visual trigger may be based on the identityof the pressed button, and the duration of the press, on the intensityof the press, and so forth. In another example, when the non-visualtrigger comprises an audible trigger, the type of the non-visual triggermay be based on sounds and/or speech included in and/or surrounding theaudible trigger.

Process 2400 may continue at step 2408, with, based on the visualtrigger and/or the non-visual trigger, transmitting a command. Forexample, the processing device(s) in the wearable apparatus 110 may beprogrammed to cause at least one transmitter (e.g., wireless transceiver530) in the wearable apparatus 110 to transmit the command. The commandmay be configured to cause a change in at least one aspect of thecontrollable device. In embodiments where the controllable device ispaired with wearable apparatus 110 (e.g., through visual pairing, asdescribed above), the command may be transmitted via the communicationpath established by the pairing.

In embodiments where wearable apparatus 110 identifies a current stateof the controllable device, wearable apparatus 110 may select thecommand from among a plurality of commands based on the identifiedcurrent state of the controllable device. For example, if thecontrollable device is identified to be in an off state, wearableapparatus 110 may select, based on the identified off state, a commandto power on the controllable device. As another example, if thecontrollable device is identified to be tuned to a particular channel orstation, wearable apparatus 110 may select, based on the identifiedchannel or station, a command to tune the controllable device to anotherchannel or station.

Alternatively or additionally, in embodiments where wearable apparatus110 identifies a context associated with the controllable device and/orthe user and/or the environment, wearable apparatus 110 may selectvalues for one or more parameters associated with the command based onthe identified context. For example, if the controllable device is amicrowave, and an identified context is associated with a meal to beprepared held by the user, the one or more parameters may includesettings for the microwave determined based on the content of the meal.As another example, if the controllable device is a vehicle, and theidentified context is associated with a user preparing to leave, the oneor more parameters may include starting the vehicle, adjusting a seatposition of the vehicle, and/or opening a garage door associated withthe vehicle based on the user that is leaving.

Alternatively or additionally, in embodiments where wearable apparatus110 identifies a type associated with the visual trigger and/or thenon-visual trigger, wearable device 110 may select the command fromamong a plurality of commands based on the identified type and thevisual trigger. For example, if the identified type is a hand gesture,and the visual trigger is an upward flick of an index finger, and thistype and visual trigger are associated with a command for turning on alight switch, wearable device 110 may select the command to turn on thelight switch.

Alternatively or additionally, in some embodiments, wearable apparatus110 may be further configured to obtain audio data captured by at leastone audio sensor included in wearable apparatus 110. The audio sensormay take the form of, for example, a microphone, and wearable apparatus110 may be further configured to process the audio data using one ormore types of audio signal processing including, but not limited to,digital signal processing and/or voice recognition techniques, such asthose based on Hidden Markov Models, dynamic time warping (DTW)techniques, and/or neural networks. In these embodiments, wearableapparatus 110 may select the command from among a plurality of commandsbased, at least in part, on the audio data. For example, if thecontrollable device is identified to be tuned to a particular channel orstation, and the audio data indicates a requested channel or station(e.g., the user 100 states aloud the name or number of a channel orstation), wearable apparatus 110 may select, based on the audio data, acommand to tune the controllable device to the requested channel orstation.

As shown in FIG. 25C, for example, based on the visual trigger 2506,wearable apparatus 110 may transmit command 2508 to controllable device2502, thereby controlling at least one aspect of controllable device2502. The controlled aspect(s) may be associated with visual trigger2506.

For example, based on the visual trigger 2506, wearable apparatus 110may transmit command 2508 to cause controllable device 2502 to changefrom an unarmed state to an armed state. As another example, based onthe visual trigger 2506, wearable apparatus 110 may transmit command2508 to cause controllable device 2502 to lock a door associated withcontrollable device 2502. As still another example, based on the visualtrigger 2506, wearable apparatus 110 may transmit command 2508 to causecontrollable device 2502 to contact emergency personnel, such as thepolice. The control may be permanent or may be temporary (e.g., command2508 may cause controllable device 2502 to power down for a period oftime or change a setting of controllable device 2502 permanently).Alternatively or additionally, controlling the aspect(s) may involvecontrolling any of the aspects described above, such as a mode ofcontrollable device 2502, a brightness and/or intensity of controllabledevice 2502, a position of controllable device 2502, or another functionof controllable device 2502.

As described above, in some embodiments, wearable apparatus 110 maydetermine a current state of the controllable device 2502. For example,wearable apparatus 110 may determine whether a door associated withcontrollable device 2502 is locked or unlocked. In these embodiments,wearable apparatus 110 may select the command 2508 from among aplurality of commands based on the identified current state ofcontrollable device 2502. For example, wearable apparatus 110 may selecta command 2508 to lock the door where it is determined that the door isunlocked, while wearable apparatus 110 may select a command 2508 to armthe security alarm where it is determined that the door is locked.

In some embodiments, wearable apparatus 110 may determine a contextassociated with the controllable device 2502. For example, wearableapparatus 110 may determine a context indicating that user 100 is goingto sleep. In these embodiments, wearable apparatus 110 may select valuesfor one or more parameters associated with the command 2508 based on theidentified context associated with the controllable device 2502. Forexample, when the context indicates that user 100 is going to sleep,wearable apparatus 110 may set parameters associated with the command2508 to lock all the doors and windows, activate a motion sensor, and/orarm the security alarm.

In some embodiments, wearable apparatus 110 may receive an indicationthat the aspect(s) has changed. For example, the controllable deviceand/or another device paired with wearable apparatus 110 may generateand provide the indication. In another example, wearable apparatus 110may analyze the stream of images to identify a change in the aspect(s)of the controllable device. After receiving the indication and/oridentifying the change in the aspect(s) of the controllable device,wearable apparatus 110 may provide feedback to user 100 indicative ofthe change in the aspect(s) of the controllable device. Example feedbackmay include visual, audio, and/or haptic feedback.

In some embodiments, wearable apparatus 110 may receive an indicationthat the command transmitted by step 2408 was received, for example bythe controlled device, by an intermediate device, by a paired device,and so forth. For example, the indication may be received as an incomingsignal using transceiver 530. After receiving the indication, wearableapparatus 110 may provide feedback to user 100 indicative that thecommand was received. Example feedback may include visual, audio, and/orhaptic feedback.

While certain environments, controllable devices, visual triggers, andaspects are shown in FIGS. 25A-25C, it will be understood that these aremerely exemplary and that other environments, controllable devices,visual triggers, and aspects are possible as well. It should be notedthat while wearable apparatus 110 may communicate commands directly toone or more controllable devices, such commands may also be provided toone or more intermediate devices. For example, in some embodiments,wearable apparatus 110 may communicate a state or command with one ormore intermediate hubs or controllers, and those hubs or controllers maydistribute subcommands to devices (e.g., lights, locks, etc.) linked ona network to the hubs or controllers, the subcommands being generatedfor accomplishing the received commands of wearable apparatus 110.

Crowd-Sourced Vision-Based Information Collection

In some embodiments, a plurality of wearable apparatuses 110 may captureimage data and may stream the image data to server 250 and/or computingdevice 120 and/or another wearable apparatus 110 for further processing.Server 250 and/or computing device 120 and/or another wearable apparatus110 may analyze the plurality of data streams to determine a traitcommon to two or more of the users of the plurality of wearableapparatuses. As such, the analysis of the plurality of data streams maybe carried out, for example, by a computing device, such as server 250,computing device 120 and/or another wearable apparatus 110. Forexemplary purposes, the analysis of the plurality of data streams isdescribed as being performed by server 250. In one example ofdetermining a trait common to two or more users wearing wearableapparatuses 110, the two or more users may face the same person, andtheir wearable apparatuses 110 may capture image data of that person.The image data may be streamed to server 250 and analyzed to determinethat the trait of the two users is an interaction with the same person.The server may store the data representing the determined trait in adatabase and/or report information relating to the trait to the user.

FIG. 26 is an illustration of an example environment 2600 includingusers (100, 101, 102) wearing wearable apparatuses 110 and a server 250capable of communicating with wearable apparatuses 110 via network 240,consistent with disclosed embodiments. Server 250 may be used inenvironment 2600 to determine traits of users 100, 101, and 102, forexample relating to image data acquired from each wearable apparatus. Insome embodiments, each wearable apparatus 110 may be configured as shownin FIG. 7. For example, wearable apparatuses 110 may include anorientation adjustment unit 705 configured to permit adjustment of imagesensor 220.

By way of example, FIG. 26 shows three users, each wearing wearableapparatus 110, experiencing different situations. Although only threeusers are shown, any number of wearable apparatuses 110 worn by usersmay communicate to server 250, for example, through network 240. Thedata sent to server 250 is, therefore, crowd-sourced because it comesfrom many different wearable apparatuses 110. Each wearable apparatus110 may acquire image-based information associated with images capturedby the camera on the particular wearable apparatus. Image-basedinformation may include raw images captured from the camera andformatted as jpeg, pic, tiff, mpeg, or any other suitable image format.Image-based information may also include images pre-processed by aprocessing device on the wearable apparatus 110. The pre-processedimages may be categorized, enhanced, compressed, or otherwise altered.Image-based information may include logos, words and/or facialinformation extracted from the images. Image-based information may alsoinclude names of products, people, etc. that the users interact with.

In some embodiments, the image-based information may be of the situationor environment of the user wearing the wearable apparatus 110. Forexample, user 100 may be shopping in a grocery store and retrieving anitem from a shelf. The wearable apparatus 110 may capture images of theuser reaching for the product (as well as nearby products) and transmitthe image-based information to server 250. In the second example, user101 may be meeting another person. Wearable apparatus 110 affixed touser 101, thus, may acquire images of the person as user 101 is facingthe person. In the third example, user 102 is looking at a computerscreen with a particular website displayed on the screen. The wearableapparatus 110 being worn by user 102 may acquire images of the computerscreen.

In some embodiments, each wearable apparatus 110 may transmitimage-based information to server 250. In some examples, wearableapparatuses 110 may first transmit the information to a network and thento server 250. In other examples, the wearable apparatuses 110 maytransmit the information directly to server 250. In some embodiments,the image-based information may be streamed to server 250. The datastream may occur in real-time (e.g., shortly after the image data isacquired, for example within one second), or the stream may be delayedby a predetermined amount of time. Thus, server 250 may receive one ormore data streams of image-based information from each wearableapparatus 110.

In some embodiments, server 250 may include a data interface (not shown)that allows server 250 to receive the data streams from wearableapparatuses 110. The data interface may include hardware and/or softwareto interface with network 240 or directly to wearable apparatuses 110.For example, the data interface may include transmitting and receivingcircuitry for a wired or wireless connection to network 240. Server 250may also include a processing device operatively coupled to memory forstoring instruction for the processing device.

In some embodiments, devices other than or in addition to server 250 mayreceive the transmitted data streams from wearable apparatuses 110. Forexample, computing device 120 may receive the data streams.Alternatively, any one or all of the wearable apparatuses 110 mayreceive the data streams from the other wearable apparatuses 110.

In some examples, server 250 may analyze the image-based information ineach of the received data streams. For example, server 250 may receive adata stream from a particular wearable apparatus 110 using the datainterface. Server 250 may, using the processing device, unpack orextract the image-based information in the data stream. In someexamples, the image-based information may be a series of images capturedby the camera on the wearable apparatus 110. In other examples, theimage-based information may include camera settings, such as f-stop,focal length, light and color content of the image, etc.

Server 250 may analyze the image-based information to determine at leastone trait of the user wearing the wearable apparatus 110. A trait inthis aspect may refer to a descriptor associated with the user wearingthe wearable apparatus 110. The descriptor may refer to any aspect oraspects of the user's interactions with the user's environment. In someexamples, a trait may involve a situation, environment, and/or activityof the user. A trait may also involve an action and/or interaction bythe user. For example, a trait may include interactions with people,interactions with certain products or types of products, activitiesengaged in, materials read and/or reviewed, etc. In some embodiments,server 250 may determine the frequency at which a trait occurs. Server250 may, for example, increment a counter each time the particular traitis determined and associate the counter with the trait. Thus, eachtrait, or category of traits, may be associated with a respectivecounter to keep track of the number of occurrences of the trait.Moreover, timers may be used to track the number of occurrences overtime. The frequency of occurrence may be associated with the trait andstored, as described below, in a database. In some examples, server 250may use image recognition algorithms and/or machine vision algorithms todetermine objects and/or persons in the received images. For example,optical character recognition (OCR) may be used to determine words in animage, such as on a paper, sign, book, etc. Detected words may be usedto recognize consumer products, brand names, and/or categories ofproducts. In some examples, edge and shape recognition may be used todetermine objects in images, such as a ball, a tree, line on a playingfield, etc. Facial recognition may be used to determine features on ahuman face, where the features may be compared to a database of featuresto determine the identity of a person. In some examples, contextanalysis may be used to determine situations involving the recognizedwords, object, people, etc. For example, an image may be captured andanalyzed to determine the presence of a ball, grass, lines on the grass,and a soccer player. Contextual analysis may then determine that theuser is attending a soccer game. Other non-exhaustive examples ofcontext analysis include: water+boat=boating; grass+ball=sport (the typeof ball may also be recognized to determine if the sport is, forexample, soccer, baseball, football, etc.); recognize lines of a playingfield; aisles+products=grocery store; read text on object to recognizebrands of products (e.g., Colgate, etc.); recognize general descriptivewords (e.g., potatoes, milk, etc.), for example, at a produce market.

Furthermore, in some embodiments, for example, analyzing images mayinvolve edge identification, in which an image is analyzed to detectpixels at which discontinuities (e.g., sudden changes in imagebrightness) occur and edges (e.g., edges of the external object) areidentified to coincide with the detected pixels. Alternatively oradditionally, in some embodiments analyzing images may involveidentifying in and/or extracting from an image pixels representative ofobjects in the environment, such as the external object. Pixels may bedetermined to be representative of an external object based on, forexample, other images of the external device or similar external devicesmaintained, e.g., in a database and/or predetermined data describing theexternal object maintained, e.g., in a database. Alternatively oradditionally, pixels may be determined to be representative of anexternal object based on, for example, a trained neural networkconfigured to detect predetermined external objects. Other types ofanalysis are possible as well, including, but not limited to, gradientmatching, greyscale matching, scale-invariant feature transform (SIFT)matching, and/or interpretation trees.

In the case where multiple images are received over a period of time,server 250 may compare sequential images to determine actors and/oractions taking place in the images. For example, as illustrated in FIG.26, user 100 is reaching towards a product on the shelf in a grocerystore. Analysis of the image-based data received from the wearableapparatus 110 being worn by user 100 may determine that the user isreaching towards a specific commercial product, such as a can of peas,and it may further be determined the particular brand of peas. Analysisof the image-based information may also determine the brands of productsnear to the particular brand that the user is interacting with. Thus,server 250 may determine that the user 100 is interacting with acommercial product and classify this interaction as a trait. Server 250may further determine that user 100 is shopping based on extractedvisual cues in the received image-based information. Thus, a separatetrait may be determined and classified as shopping. In some examples,server 250 may also use non-visual information in order to obtain suchdeterminations. Examples of such non-visual information may include, butare not limited to: financial information, including billinginformation; positioning information, such as geolocation data; temporalinformation, including time of day, date, etc.; information obtainedfrom a calendar and/or day planner; information obtained from a socialnetwork; information based on captured audio; and so forth.

In some embodiments, server 250 may determine the frequency at whichuser 100 interacts with commercial products. For example, server 250 maykeep track of the number of times a certain trait occurs with the user.Server 250 may, for example, increment a counter each time theparticular trait is determined and associate the counter with the trait.The frequency of occurrence may be associated with the trait and stored,as described below, in a database.

Similarly, server 250 may analyze the received data stream from wearableapparatus 110 being worn by user 101. In this example, user 101 isinteracting with a person (e.g., shaking hands, a face appearing withina predetermined distance, detection of eyes having a looking directiontoward the user, etc.). The image-based information received from user101 may be analyzed to determine several aspects of the interaction. Forexample, the identity of the person may be determined using facialrecognition. The type of interaction with the person may also bedetermined. For example, edge and contour recognition may determine thatuser 101's arm and the other person's arm are meeting between theirbodies, and context analysis may be used to deter mine that a handshakeis taking place. The mood of the person may also be determined by, forexample, employing trained neural networks to determine characteristicsrelated to mood, such as furrowed brow—angry, smiling, laughing,tears/crying—sad etc. The environment surrounding the interaction mayalso be determined by recognizing objects and landscapes, etc. Thus, inthe example shown, server 250 may determine that the user 101 isinteracting with a person (e.g., greeting the person by shaking hands)and classify this interaction as a trait. Again, multiple traits may bedetermined from the same situation. For example, the trait may beclassified as a “greeting,” a “salutation,” a “business event” (if thedata indicate that the person is a business colleague), etc. In someexamples, server 250 may also use non-visual information in order toobtain such determinations, as described above. For example, server 250may use information from a calendar and/or day planner to identify thenature of the interaction, the identity of the person, and so forth.Along the same lines, server 250 may receive a data stream from wearableapparatus 110 being worn by user 102. In this case, user 102 is lookingat a computer screen where a particular website is being displayed.Server 250 may receive images of the website as content in the datastream. Server 250 may analyze the received data stream to determineand/or identify the website. For example, server 250 may determine theURL of the website, recognize images on the web page and compare theimages to known images for particular websites, recognize logos, read anentity name from the displayed page, etc., and determine that user 102is visiting the identified website. Thus, server 250 may determine thatthe user 102 is visiting a particular website and classify thisinteraction as a trait. Furthermore, in some examples, server 250 mayanalyze the received data stream to determine one or more activitiesassociated with the website, such as searches, posting, and so forth.

In some embodiments, server 250 may determine the frequency at whichuser 102 visits a particular website, interacts with a particularperson, performs a particular task, chooses a particular consumerproduct, etc. As explained above, timers and counters associated withparticular traits or categories of traits.

In other examples, the image-based information may relate to a handgesture captured by wearable apparatus 110 (not shown in FIG. 26). Forexample, the user may point to an object, swipe in a particulardirection in front of the camera, wave to a person, etc. The handgesture captured in the image-based information may indicate an actionto be taken, such as retrieving information about a product, saving alocation of an item, deleting an item for a database, etc. The handgesture may also be used by server 250 in contextual analysis of asituation. For example, if a user waves to another person, server 250may determine that the user and the person are friends. Wearableapparatus 110 may acquire images of the hand gesture and stream theimage-based information to server 250. Server 250 may analyze theimage-based information to determine the gesture performed. In someexamples, server 250 may analyze image-based information from two ormore wearable apparatuses 110 to determine the gesture. For example, thewearable apparatus of a first user may capture partial images of thehand gesture such that the gesture is not fully recognized. A seconduser with a different wearable apparatus may be in the same environmentand also capture images of the first user's hand gesture. Server 250 mayrecognize that the images from the second user include a hand gesturefrom the first user and subsequently recognize the hand gestureassociated with the first user.

Several examples of traits have been described above, but other traitsare possible. Further examples of traits may be: experiencing a sportingevent (e.g., the field, type of ball, uniforms, etc. may be recognizedto determine particular sporting event), a birthday party (e.g.,objects, such as a cake, candles, decorations, etc. may be recognized),eating a meal (e.g., the location, logo of a restaurant, type of foodmay be recognized), driving a car (e.g., steering wheel may berecognized along with acceleration occurring when the images werecaptured), etc.

In some embodiments, two or more users may be experiencing or may haveexperienced the same or similar situation (e.g., trait). In this case,server 250 may determine that two or more users have at least one traitin common. For example, two of the users may be shopping in the samegrocery store. Server 250 may analyze the data streams from the twousers and determine that both users have interacted with the samecommercial product. Thus, the common trait may be identified. In someexamples, the common trait may be interaction with the same person,performing the same action (e.g., shaking hands), visiting the samewebsite, etc.

In some embodiments, server 250 may analyze the received data streams inparallel to determine the common trait. For example, image-basedinformation with images of the same situation from two or more wearableapparatuses 110 may be analyzed. Server 250 may recognize that theimages provide views from different angles of the same environment. Forexample, server 250 may use location coordinates of where the imageswere taken to determine that they are of the same environment. Server250 may also recognize names of places or other location identifiers todetermine that the images are of the same environment. In otherexamples, server 250 may use timestamps to determine that the imageswere taken at the same or similar time. Furthermore, server 250 mayrecognize the same objects or persons in images from the different datastreams. Analyzing the images from the multiple data streams in the sameenvironment may aid in determining the trait for each user. For example,the image-based information may be correlated to, for example, recognizeindividuals, products, activities, contexts, etc. from different angles.Moreover, server 250 may also determine the frequency at which each userexperiences the particular trait. For example, server 250 may determinea frequency or an average time of interaction for the common trait andstore the frequency or average time, as described below.

In some embodiments, server 250 may store the information relating tothe determined trait or traits in a storage resource 2610. The storageresource 2610 may be local to server 250, at a remote location,distributed (e.g., in multiple locations on part of multiple systems),and so forth. In some examples, the storage resource 2610 may includeinternal memory of server 250 and/or computing device 120. Storageresource 2610 may also include memory within one or more of wearableapparatuses 110. In some examples, storage resource 2610 may be anonvolatile storage medium, such as a hard disk or a solid state disk.Server 250 may store the information relating to the determined trait ortraits in a database on storage resource 2610. The database may be ofany known kind, such as a relational database or a self-referencingdatabase. The database may include demographic information about theuser or users associated with the trait or traits. For example, adatabase entry containing information relating to the determined traitor traits may also contain biographical information (e.g., demographics)about the user or users associated with the trait or traits.

In some embodiments the stored information and/or information based onthe stored information may be output to a user. For example, server 250may include a monitor 2620 and display a graphical user interface (GUI)containing the information relating to the determined traits. In otherexamples, the stored information may be displayed on computing device120. In still other examples, the stored information may be presented tothe user through a display on a wearable apparatus. In some embodiments,the stored information may be communicated to a user audibly. Forexample, the results of a query to the database may be read by devices,such as server 250, computing device 120, and/or wearable apparatus 110.The displayed or otherwise communicated information may include thedetermined trait and/or demographics information about the users of thewearable apparatuses to which the trait belongs.

In some embodiments, server 250 may receive a query pertaining to thestored information. Server 250 may respond to the query by transmittinginformation requested in the query to the requesting device. Forexample, computing device 120 may send a query to server 250 requestingparticular information about a trait, such as shopping in a grocerystore. Server 250 may access the database containing the storedinformation, sort the information and organize a response. For example,the response may contain the most common commercial product chosen, themost active grocery store, where in a grocery store a particular item islocated, etc.

In some embodiments, the data stream may include more information thanjust image-based information. For example, the data stream may alsoinclude position information, recognizing the location of where theimages were captured. In some examples, position information may includeglobal position system information (e.g., latitude and longitude),proximity to Wi-Fi hotspots or cellular towers, or other absolutepositioning information. Position information may also include names oflocations where the images were captured. For example, a user may inputlocation information, such as “home” or “work” or “Leaning Tower ofPisa.” In other examples, image recognition may be used to identify thelocation of images. Wearable apparatus 110 and/or server 250 may analyzethe images and determine a location based on identifying characteristicsof the images. By recognizing specific objects, places, words, etc. inthe images. For example, the words “Hank's Supermarket” may berecognized on an identified building that the user walks toward. Server250 may search a database to determine the location of “Hank'sSupermarket” and assign a location to the images.

In some embodiments, server 250 may analyze the received data streamcontaining position information to determine the position of wearableapparatus 110. For example, the processing device may be programmed toextract the position information from the one or more data streamsreceived from wearable apparatuses 110, and based on the positioninformation, determine the location of each wearable apparatus. Server250 may store the determined location of the wearable devices in thedatabase as additional information about the determined trait or traits.

In some embodiments, the one or more data streams may include timinginformation, which may identify when a particular image is captured by awearable apparatus 110 or the duration between captured images. Forexample, each wearable apparatus 110 may provide a timestamp (includinga date and/or time) associated with each captured image. The timestampmay be integrated into the image-based information and/or associated aparticular image by reference. In some examples, server 250 may extractthe timing information to order received image-based information priorto or while analyzing the image-based information to determine the traitor traits.

In some embodiments, interaction frequency, schedule, and/or durationmay be determined, in part, using the timing information. For example,timing information may be associated with the determination of a traitfor each user. Based on the trait timing information, server 250 maydetermine a pattern of interaction, such as a schedule, and associatethe schedule with the user and trait. In other examples, timinginformation may be used to determine how long a particular trait lasts.For example, timing information may be used to determine how long a user(e.g., user 102) spends on a particular website.

In some embodiments, the one or more data streams may includeinformation based on audio captured from an environment of wearableapparatus 110. For example, the information based on audio may includeconversation topics, transcriptions, noise level, musical genre, and soforth. In some examples, the information based on audio may be used todetermine a type of interaction a user is involved in, a preference ofthe user, and so forth.

In some embodiments, the one or more data streams may include motioninformation related to motion of the wearable apparatus 110. Forexample, each wearable apparatus 110 may include a motion sensor, suchas a multi-axis accelerometer, gyroscope (e.g., MEMs-based), pressuresensor, magnetic sensor (e.g., compass), etc. Motion information may beassociated with the captured images in the image-based informationand/or include in the data stream with reference to captured images.Because the motion information may correspond to motion of the wearableapparatus, the motion information may also be unassociated with capturedimages and transmitted in the data stream as independent data. In someembodiments, server 250 may receive the motion information as part ofthe received data stream. Server 250 may extract the motion informationfrom the data stream and use the information to determine the trait ortraits of each user. For example, server 250 may determine that user 101walks towards the person before shaking hands. The received motioninformation would indicate that the handshake is a greeting and thetrait may be classified accordingly. In another example, the motioninformation may be used to determine or confirm participation inparticular activities. For example, server 250 may recognize a soccerball on a grass field and tentatively determine that the user is playingsoccer. Server 250 may confirm the tentative determination by analyzingthe motion information and proximity of the ball to confirm that theuser is playing soccer.

FIG. 27A illustrates an exemplary embodiment of memory 2710 containingsoftware modules to determine a trait common to two or more users. Forexample, one or more of server 250, computing device 120, or wearableapparatus 110 may execute instructions from the modules to perform oneor more of the functions as described with respect to FIG. 26, above.Included in the memory are receiving module 2711, analysis module 2712,and storing module 2713. Modules 2711, 2712, and 2713 may containsoftware instructions for execution by at least one processing deviceincluded with a server-based system, such as is included in server 250.

Receiving module 2711 may be configured to receive one or more datastreams transmitted by wearable apparatuses. Receiving module 2711 mayinteract with a data interface to receive the one or more data streams.Receiving module 2711 may control the data interface to receive multipledata streams simultaneously from one or more transmission sources. Forexample, data streams may be received through a wired connection orthrough a wireless connection or through both.

Analysis module 2712 may be configured to analyze the one or morereceived data streams to determine one or more traits associated withdifferent users wearing wearable apparatuses originating the datastreams. Analysis module 2712 may extract information from the datastreams to aid in determination of specific traits of the users. Forexample, analysis module 2712 may extract image-based information,position information, timing information, and/or motion information fromthe data streams. Analysis module 2712 may use facial detection andrecognition to determine traits, such as interaction with other persons.Analysis module 2712 may also use machine vision algorithms to determinetraits such as identifying commercial products, landscape, objects,locations, etc. Furthermore, analysis module 2712 may use positioninformation to determine a location of a trait; timing information todetermine frequency, scheduling, and/or duration of a trait; informationbased on audio; and motion information to determine specific interactionstates, such as movement towards an object.

Storing module 2713 may be configured to store information relating to atrait, such as a trait that is common to two or more user wearingwearable apparatuses. Storing module 2713 may include instructions tointeract with internal or external storage resources, such as memory,solid state hard drives, or removable memory devices. Storing module2713 may also interact with the data interface to access remote storageresources such as computing device 120 or wearable apparatuses 110.Storing module 2713 may also store information relating to thedetermined traits in cloud computing storage devices.

FIG. 27B illustrates the contents of a data stream 2720 received by aprocessing device, such as server 250, computing device 120, or wearableapparatus 110. The data stream 2720 may consist of packet or frames thatare transmitted by any suitable transmission means. For example, thedata stream 2720 may be transmitted through the Internet using a TCP/IPprotocol, over wireless connection using Bluetooth®, or other suitabletransmission means. The packets or frames may be divided into segmentscontaining image-based information 2721, position information 2722,timing information 2723, and/or motion information 2724. As shown inFIG. 27B, position information 2722, timing information 2723, and motioninformation 2724 are optional, as depicted by the dashed line. In someexamples, position information 2722, timing information 2723, and/ormotion information 2724 may be included in image-based information sothat the data stream 2720 would use only one segment. In other examples,position information 2722, timing information 2723, and/or motioninformation 2724 may be associated with image-based information 2721,such as by reference. The association of position information 2722,timing information 2723, and/or motion information 2724 may be to theimage-based information in the same or different packets or streams. Insome examples, the data stream may contain a header identifying thewearable apparatus 110 from which the data stream is transmitted. Theheader may also contain demographic information about the user wearingthe wearable apparatus 110.

In some examples, image-based information 2721 may comprise one or moreimages captured by the camera in wearable apparatus 110. The one or moreimages may be compressed and may be represented in any suitable format,such as JPEG, TIFF, PDF, GIF, PNG, BMP, SVG, etc. In some examples,image-based information 2721 may comprise video in any suitable format,such as MPEG, AVX, MOV, etc. Image-based information 2721 may alsocontain meta-data with additional image related information, such ascamera settings, location, time, etc. Alternatively, in someembodiments, image-based information 2721 may include informationderived from analysis of one or more images (e.g., a description of acontext of an image, an identifier of a person, object, or location inan image, etc.). In some examples, image-based information 2721 maycomprise information derived from the one or more images captured by thecamera by wearable apparatus 110.

Position information 2722 may be included in data stream 2720. Positioninformation 2722 may include GPS location coordinates of the camera inwearable apparatus 110. Position information may also include othertriangulated coordinates, such as derived from Wi-Fi, cellular towers,etc. Positional information 2722 may be embedded in image-basedinformation 2721 or be included in a separate segment of data stream2720.

Timing information 2723 may be included in data stream 2720. Timinginformation 2723 may include absolute or relative time. For example,Timing information 2723 may include the time of day and/or date that animage was captured. Timing information 2723 may also include the timesince a last image was captured, providing relative timing informationbetween images. Timing information 2723 may be embedded in image-basedinformation 2721 or be included in a separate segment of data stream2720.

Motion information 2724 may be included in data stream 2720. Motioninformation 2724 may include acceleration information in multipledirection (e.g., 3-axis, 6-axis, 9-axis), speed information, or otherdata that indicates motion of wearable apparatus 110. Motion information2724 may be embedded in image-based information 2721 or be included in aseparate segment of data stream 2720.

In some examples, other types of information may be included in datastream 2720. For example, data stream 2720 may include audio information2725. Audio information 2725 may include audio captured by wearableapparatus 110 and/or information based on audio captured by wearableapparatus 110.

FIG. 28 is a flowchart illustrating an exemplary method 2800 ofdetermining and storing a trait of a user or a trait common to two ormore users consistent with the disclosed embodiments. The method may beimplemented in a system such as shown in FIG. 5 and/or FIG. 26.

At step 2805, one or more data streams are received. In some examples,the data streams may be received from wearable apparatuses 110 and beconfigured as described above in connection with FIG. 27B. The one ormore data streams may be received from one or more wearable apparatuses110. The one or more data streams may contain image-based information,position information, timing information, motion information, audioinformation, and so forth. The one or more data streams may be sent inpackets, frames, and so forth. The data streams may be continuouslyreceived over time, received at predetermined intervals, received atselected times, received in response to a trigger, and so forth.

At optional step 2810, the contents of the one or more data streams maybe extracted. For example, the data streams may contain image-basedinformation, position information, timing information, and/or motioninformation. The data streams may be processed by a processor device toextract the type of information desired for further processing.

At step 2815, the one or more data streams may be analyzed to determineat least one trait. In some examples, the trait is common to two or moreusers from which the data streams are received. In some embodiments,server 250 may analyze the data streams. In other embodiments, computingdevice 120 or wearable apparatus 110 may analyze the data streams. Forexample, the image-based information in the data streams may be analyzedby executing instructions stored in analysis module 2721. In someembodiments, the image-based information may be analyzed to determinewhether a trait identified in a data stream received from one user iscommon with a trait identified in a data stream received from adifferent user. In some embodiments, position information, timinginformation, and/or motion information may be analyzed in addition toimage-based information in order to determine a trait. For example,image-based information may be analyzed using machine vision or imagerecognition algorithms to determine that a user's hand is near acommercial product. Timing and motion information may be used todetermine that the user's hand is moving towards the commercial product.Thus, a trait of choosing a commercial product may be determined. Insome examples, two or more users may be choosing the same or similarproduct. Therefore, the trait is determined to be common to the two ormore different users.

At step 2820, information related to the at least one identified traitmay be stored. The at least one identified trait may be common to two ormore different users. The at least one identified trait may be stored ina database, such as a relational database or self-referencing database.The database may be stored, for example, in server 250, computing device120, wearable apparatus 110, among distributed systems, or in a separatestorage resource, such as a cloud computing device. In some embodiments,the stored information relating to the at least one trait, orinformation based on the stored information, may be output to agraphical display. For example, the information may be output to agraphical display of a device (e.g., computing device 120) paired withwearable device 110 and/or to a display associated with server 250(e.g., monitor 2620).

Context-Based Suggestions Through Image Analysis

In some embodiments, apparatus 110 may cause a paired device, such ascomputing device 120, to provide one or more alerts to user 100 based oninformation determined from an identified contextual situation. Asmentioned above, contextual situations may refer to a combination ofcircumstances that may influence the user's action. Examples of factorsthat may differentiate contextual situations include: the identity ofother people in the vicinity of user 100 (e.g., certain individual,family members, coworkers, strangers, and more), the type of activityuser 100 is doing (e.g., watching a movie, meeting with an individual,visiting a location, interacting with an object, entering a car,participating in a sport activity, eating a meal, and more), the time inwhich the situation took place (e.g., the time of the day, the time ofthe year, and more), the location in which the situation occurs (e.g.,home, working place, shopping mall, and more). During a typical day,user 100 may experience dozens, if not hundreds, of contextualsituations. Identifying these contextual situations can assist incategorizing and organizing the personal experiences of the user's life.Moreover, consistent with this aspect of the disclosure, apparatus 110can analyze in real-time the images captured from the environment ofuser 100 to identify a current contextual situation. By identifyingcontextual situations substantially in real-time, apparatus 110 canprovide added value to user 100. For example, after identifying acontextual situation, apparatus 110 may cause computing device 120 toprovide one or more alerts to user 100. In some cases, the task ofdetermining which alerts to provide user 100 may be very complex,because of the huge amount of similar contextual situations that user100 experiences every day and the desire to provide only relevantalerts. For example, when the contextual situation includes anidentification of a worker at a work site, apparatus 110 may provide analert that indicates that the worker is not using and/or wearing safetyequipment. Additional exemplary embodiments of contextual situations andthe type of alerts that may be provided to user 100 are discussed infurther detail with respect to FIGS. 29A-29D.

FIG. 29A is a schematic illustration of a contextual situation that maytrigger provisioning of an alert consistent with the present disclosure.The contextual situation illustrated in this figure is transitioningfrom indoors to outdoors. Apparatus 110 may identify this contextualsituation by analyzing a plurality of images, such as image 2900. Afteridentifying this contextual situation, apparatus 110 may cause computingdevice 120 to provide different alerts to user 100.

One type of alert that may be provided to user 100 includes a suggestion2902 to remember a key. In one example, suggestion 2902 may include thelast captured image of the key 2904. Another type of alert provided touser 100 includes a suggestion (not shown) to remember rain gear. In oneembodiment, prior to providing this alert, computing device 120 maycheck the weather forecast online to determine the likelihood that arain gear will be needed. Another type of alert that may be provided touser 100 includes a suggestion (not shown) to change the heating,ventilation, and air conditioning (HVAC) settings. In one embodiment,prior to providing this suggestion, apparatus 110 may determine thatthere are no other individuals indoors. Another type of alert that maybe provided to user 100 includes a suggestion (not shown) to feed a cat.

User 100 may transition from indoors to outdoors numerous times a day.Not all the alerts may be relevant to all of the users, and not all thealerts will be relevant all the time. In some embodiments, apparatus 110(or computing device 120) may determine which alerts to provide and whento provide them to user 100. For example, apparatus 110 may causecomputing device 120 to provide user 100 with suggestion 2902 toremember his/her key in some cases and avoid providing user 100 withsuggestion 2902 in other cases. In order to determine which cases arerelevant, apparatus 110 (or computing device 120) may use predefinedcontext rules. The context rules may be determined over time usingmachine learning, be selected by user 100, or may be the result ofdefault settings. Examples of context rules may include providing user100 with suggestion 2902 only when user 100 leaves his/her own house, oronly when the transitioning from indoors to outdoors occurs between 6a.m. and 9 a.m., or only if apparatus 110 determined that user 100didn't hold his/her keys in the last 30 minutes. Apparatus 110 (orcomputing device 120) may apply different combinations of these contextrules using logical operators (e.g., AND, OR, NOT, etc.) to determinewhen to provide user 100 the alert. For example, suggestion 2902 may beprovided only when user 100 leaves his/her own house AND the time isbetween 6 AM and 9 AM.

FIG. 29B is a schematic illustration of another contextual situationthat may trigger provisioning of an alert consistent with someembodiments of the present disclosure. The contextual situationillustrated in this figure is exiting a vehicle. Apparatus 110 mayidentify this contextual situation by analyzing a plurality of images,such as image 2910. After identifying this contextual situation,apparatus 110 may cause computing device 120 to provide different alertsto user 100.

One type of alert that may be provided to user 100 may include areminder 2922, indicating that a child is present in the vehicle. Insome embodiments, apparatus 110 may cause computing device 120 toprovide reminder 2922 after a determination was made that the child isindeed present in the vehicle. For example, apparatus 110 may havepreviously captured images of the child in the vehicle after user 100entered the vehicle. Another type of alert provided to user 100 mayinclude a suggestion (not shown) to pay for parking using a parkingapplication. In some embodiments, apparatus 110 may cause computingdevice 120 to provide this suggestion after a determination was madethat the vehicle is parked in area where payment is required. Additionaldetails regarding the determination process is provided below withreference to FIG. 30.

FIG. 29C is a schematic illustration of yet another contextual situationthat may trigger provisioning of an alert consistent with someembodiments of the present disclosure. The contextual situationillustrated in this figure is entering a grocery store. Apparatus 110may identify this contextual situation by analyzing a plurality ofimages, such as image 2920. After identifying this contextual situation,apparatus 110 may cause computing device 120 to provide different alertsto user 100.

One type of alert that may be provided to user 100 may include asuggestion 2922 to purchase one or more items. The one or more items maybe previously identified by apparatus 110 as products that user 100needs or wishes to buy. Specifically, suggestion 2922 may be providedbased on a determination using prior captured images, that a containerassociated with the item was discarded by user 100. For example,apparatus 110 may have previously identified that user 100 threw anempty container of milk into a waste receptacle, which willautomatically cause the milk product to be included in the grocery list.Alternatively, the one or more items may have been previously identifiedby user 100 as products that he/she wants to buy. For example, user 100may see an interesting recipe in a book and use a predefined handmovement or voice command to indicate a selection of this recipe.Another type of alert provided to user 100 may include a coupon 2924 fora specific product. Apparatus 110 may use computing device 120 to searchfor coupons available in the specific grocery store that user 100entered to and which may have value to user 100.

FIG. 29D is a schematic illustration of still another contextualsituation that may trigger provisioning of an alert consistent with someembodiments of the present disclosure. The contextual situationillustrated in this figure is a document present in an area in front ofuser 100. The document in front of user 100 may be printed on paper ordigitally displayed on a screen. Apparatus 110 may identify thiscontextual situation by analyzing a plurality of images, such as image2930. After identifying this contextual situation, apparatus 110 maycause computing device 120 to provide different alerts to user 100.

In the embodiment illustrated in FIG. 29D the document in front of user100 is a business card. In this embodiment, apparatus 110 may transmitdetails (e.g., text and/or image) from the business card as thedetermined information associated with the contextual situation. In someembodiments, text may be determined using optical character recognition(OCR). Transmitting the details to computing device 120 may cause thecomputing device 120 to add a new contact based on the received details.In addition, apparatus 110 may be configured to transmit the details tocomputing device 120 in response to a visual trigger or a voice commandfrom user 100. For example, the visual trigger may include placing thebusiness card opposite to apparatus 110 for a predefined period of time,such as three seconds. In another embodiment, the document may includean address and apparatus 110 may transmit the address appearing on thedocument as the determined information associated with the contextualsituation. Transmitting the address to computing device 120 may causethe computing device 120 to add the address to a user interfaceassociated with a navigation assistance application. For example, thedocument may be an invitation to an event and after apparatus 110recognized the invitation in front of user 100, apparatus 110 may causethe user's smartphone to start navigating to the location of the event.In another embodiment, the document in front of user 100 is a financialdocument. In this embodiment, apparatus 110 may transmit details (e.g.,text, numbers, figures) from a financial document as the determinedinformation associated with the contextual situation. Transmitting thedetails from the financial document to computing device 120 may cause atleast some of the details from the financial document to appear in auser interface associated with a financial-related application oncomputing device 120. For example, the financial document may be a bankaccount check, and once apparatus 110 recognizes the check in front ofuser 100, it may causes the user's smartphone to open his/her onlinebanking application and starts the process of depositing the check.

A person skilled in the art can appreciate that many more types ofcontextual situations (not shown in the figures) may trigger event moretypes of alerts consistent with the present disclosure. For example, asdiscussed above, the contextual situation may include an identificationof at least one worker at a work site and the alert indicates that theworker is not using and/or wearing safety equipment. The presentdisclosure is not limited to the disclosed exemplary contextualsituations and exemplary alerts. In addition, the different types ofalerts discussed above with respect to FIGS. 29A-29D may be part ofpredefined functions that were triggered by the transmission ofimage-related information. The image-related information may bedetermined based on a request associated with a category tag from thepaired device, as discussed above with respect to FIG. 19.

As discussed above, apparatus 110 may analyze the plurality of images toidentify a contextual situation related to user 100. Consistent withpresent disclosure, analyzing the plurality of images to identify thecontextual situation may include executing a region-of-interest (ROI)analysis to detect at least one object in an image. The at least oneobject in the image may be represented by one or more ROI that can bevaried in types and formats. Examples of region of interest descriptionsinclude, but are not limited to bounding boxes, masks, areas, andpolygons. Apparatus 110 may detect the at least one object using a ROIdatabase that contains searchable fields and vectors representing theimage characteristics of objects. In some embodiment, each entry in theROI database may have a data structure that includes one or more of thefollowing components: an ROI type field that contains the type of theregion of interest, an ROI descriptor field that contains thedescription of the region of interest, a color vector, a shape vector, atexture vector, a size vector, a location vector, and a content vector.Apparatus 110 may detect the at least one object in the image bycomparing the one or more ROI that are found in the image with the ROIfound in the ROI database. After detecting the at least one object inthe image, apparatus 110 may start the process of identifying thecontextual situation. In one embodiment, each contextual situation mayinclude data identifying one or more objects that a particular type ofcontextual situation has to include, data identifying one or moreobjects that the particular type of contextual situation may include,and data identifying one or more objects that the particular type ofcontextual situation could not include. By considering the detectedobjects in the image, apparatus 110 may select a plurality of candidatecontextual situations that may be appropriate for the image beinganalyzed. To improve the certainty level, apparatus 110 may useimage-information from previously obtained images, and additionalinformation that does not originate from image data, such as,information from a Global Positioning System (GPS). In one embodiment,apparatus 110 may rank the plurality of candidate contextual situations.Accordingly, in one embodiment, the identified contextual situation maybe the highest ranking contextual situation from the plurality ofcandidate contextual situations.

Furthermore, in some embodiments, for example, analyzing the pluralityof images may involve edge identification, in which an image is analyzedto detect pixels at which discontinuities (e.g., sudden changes in imagebrightness) occur and edges (e.g., edges of the external object) areidentified to coincide with the detected pixels. Alternatively oradditionally, in some embodiments analyzing the plurality of images mayinvolve identifying in and/or extracting from an image pixelsrepresentative of objects in the environment, such as the externalobject. Pixels may be determined to be representative of an externalobject based on, for example, other images of the external device orsimilar external devices maintained, e.g., in a database and/orpredetermined data describing the external object maintained, e.g., in adatabase. Alternatively or additionally, pixels may be determined to berepresentative of an external object based on, for example, a trainedneural network configured to detect predetermined external objects.Other types of analysis are possible as well, including, but not limitedto, gradient matching, greyscale matching, scale-invariant featuretransform (SIFT) matching, and/or interpretation trees.

FIG. 30 is a flowchart of an example process 3000 for providing one ormore alerts to user 100. The process starts when apparatus 110identifies a contextual situation (block 3002) and ends when computingdevice 120 provides one or more relevant alerts to user 100 (block3022). In one example, the one or more relevant alerts may include allthe alerts determined to be relevant to user 100. The identification ofthe contextual situation may be done by apparatus 110 when apparatus 110analyzes the plurality of images captured from an environment user 100.Relevant alerts may be provided by computing device 120 as a visualoutput or as an audible output.

FIG. 30 depicts two ways to complete process 3000. The first way, markedas path “A,” is to transmit information about the identified contextualsituation to computing device 120 (block 3004) and thereafter continuingthe process (blocks 3006-3022) at computing device 120. The second way,marked as path “B,” is to continue the process (blocks 3006-3018) atapparatus 110 and afterwards transmit information related to the one ormore relevant alerts to computing device 120 (block 3020).

A processing device, either processor 210 or processor 540, may access aplurality of alerts associated with the identified contextual situation(block 3006). The alerts may be stored in an associated memory device,for example, memory 550 or memory 550 b. For example, in case theidentified contextual situation is transitioning from indoors tooutdoors, the potential alerts may include: a suggestion to remember akey, a suggestion to remember rain gear, a suggestion to change an HVACsetting, a suggestion to feed a pet, and more. The processing device mayselect one alert associated with the identified contextual situation(block 3008), for example, the suggestion to change an HVAC setting.

Next, the processing device may access a plurality of context rulesassociated with the selected alert (block 3010). The plurality ofcontext rules may be also stored in the associated memory device. Forthe selected alert “suggestion to change an HVAC setting,” the pluralityof context rules may include: provide the suggestion when the identifiedcontextual situation occurs took place at a predefined location (e.g.,home, work), provide the suggestion when the identified contextualsituation occurs at a predefined period of time (e.g., between 6 a.m.and 9 a.m., or after 7 p.m.), provide the suggestion when there are noother individuals indoors, provide the suggestion when there is anindication that HVAC is working, and more. In other words, the contextrules may be used to define the combination of conditions in which itwould be appropriate to provide a certain alert for the identifiedcontextual situation.

Thereafter, the processing device may retrieve information related tothe plurality of context rules (block 3012). In one embodiment, theretrieval of information related to the plurality of context rules maybe based on prior captured images. For example, to determine that thereare no other individuals indoors, the processing device may reviewcaptured images from the last 15 minutes. In another embodiment, theretrieval of information related to the plurality of context rules maybe based on additional sensors and/or publicly available data sources.For example, to determine that the HVAC is working, processing devicemay determine the indoors temperature using a sensor in the paireddevice, and the outdoors temperature using an online database. Then, theprocessing device may make a determination whether the selected alert isrelevant based on the plurality of context rules and the retrievedinformation (block 3014). In some embodiments, the processing device mayfacilitate machine learning algorithms to determine the likelihood thatproviding the alert will caused user 100 to perform an action. Alertsthat are determined to have a likelihood of relevant exceeding, forexample, a threshold may be classified as relevant alerts to aparticular user. For example, when user 100 leaves a work location at 7PM, a relevant alert might be to turn off the HVAC, while a non-relevantalert might be to feed the user's pet.

If the relevant alerts have been identified (decision block 3016), theprocessing device may aggregate information about one or more alertsdetermined to be relevant (block 3018) and transmit the one or morerelevant alerts to a paired device (e.g., computing device 120) (block3018). If all of the relevant alerts have not been identified, theprocessing device may select another alert from the list of alerts andrepeat blocks 3010 to 3014. In embodiments proceeding according to path“B,” following block 3020, the process may proceed to block 120, and thepaired device (e.g., computing device 120) may provide the one or morerelevant alerts to the user (e.g., by displaying the alert on a displayof computing device 120). In embodiments proceeding according to path“A”, following block 3018, the process may proceed to block 3022, theone or more relevant alerts may be provided to the user via computingdevice 120.

FIG. 31 is a flowchart showing an exemplary process 3100 for identifyinga contextual situation related to user 100, consistent with disclosedembodiments. Apparatus 110 or computing device 120 may implement one ormore steps of process 3100 to identify contextual situations illustratedin, for example, FIGS. 29A-29D.

As illustrated in FIG. 31, at step 3110, a processing device (e.g.,processor 210, processor 540) may receive a plurality of images from anenvironment of user 100. At step 3120, the processing device may analyzethe plurality of images to identify a contextual situation related touser 100. At step 3130, the processing device may determine informationassociated with the contextual situation. After determining theinformation associated with the contextual situation, at step 3140, theprocessing device may cause a paired device (e.g., computing device 120)to provide at least one alert to user based on the determinedinformation associated with the contextual situation. These steps ofprocess 3100 are discussed in further detail below.

Specifically, at step 3110, the processing device may receive aplurality of images from an environment of user 100. In some embodimentsthe plurality of images are captured by image sensor 220. The field ofview of image sensor 220 may include an area in front of user 100. Forexample an optical axis of image sensor 220 may extend generally in awalking direction of user 100. The field of view of image sensor 220 maybe more than 100°, for example, more than 120°, or more than 150°. Theimage sensor may be included in a capturing unit (such as capturing unit710). In addition, apparatus 110 may include a directional microphoneconfigured to detect or receive a sound or sound wave. Accordingly, theprocessing device may receive a plurality of images from an environmentof user 100 associated with an audio data stream.

A step 3120, the processing device may analyze the plurality of imagesto identify a contextual situation related to user 100 (e.g.,transitioning from indoors to outdoors, transitioning from outdoors toindoors, entering a grocery store, exiting a grocery store, entering avehicle, exiting a vehicle, detection of one or more persons,identification of one or more persons, a document present in an area infront of user 100, etc.). In some embodiments, analyzing the pluralityof images may include determining an existence of elements in theplurality of images that may be indicative of a contextual situation(e.g., existence of a door), and comparing the determined elements withthe sample elements in the database (e.g., an image sample of the doorin the user's home). After determining the existence of elements, theprocessing device may be programmed to retrieve additional informationfrom one or more sources that may assist in identifying or improving thecertainty level in the identification of the contextual situation. Forexample, the processing device may access location information from aGlobal Positioning System (GPS) to confirm that user 100 is currentlyexiting his/her own home. In another example, the processing device maybe programmed to analyze the audio data stream to assist in identifyingor improving the certainty level in the identification of the contextualsituation. Consistent with the present disclosure, analyzing part of thepictures captured by image sensor 220, may help reduce the time neededto identify the contextual situation. For example, repetitive images ofthe environment of user 100 may not all be necessary for identifying acontextual situation related to user 100. Two images may be consideredrepetitive when the images show the same, similar, or overlapping imagecontent. Therefore, when analyzing the plurality of images, theprocessing device may perform methods to discard and/or ignore at leastsome of the repetitive images.

At step 3130, the processing device may determine information associatedwith the contextual situation. The determined information may bespecific to the identified contextual situation. In addition, thedetermined information may be associated with the types of alerts and/orthe list of context rules associated with each contextual situation. Forexample, when the contextual situation is “transitioning from indoors tooutdoors,” the determined information may be: “where and when was thelast appearance of the user's keys in the captured images.” In anotherexample, when the contextual situation is “a document present in an areain front of the user,” the determined information may be: “an addressappearing on the document.” In some embodiments, the determinedinformation may be determined using information derived from previouslycaptured images. In other embodiments, the determined information may bedetermined using information from multiple independent sources, such asadditional sensors in apparatus 110, additional sensors in computingdevice 120, and/or information stored in a remote database.

At step 3140, the processing device may cause a paired device to provideat least one alert to a user based on the determined informationassociated with the contextual situation. The paired device (e.g.,computing device 120) may be at least one of: a smartphone, a tablet, asmarthome controller, or a smart watch. When the processing device ispart of apparatus 110 (e.g., processor 210), step 3140 may includecausing a transmitter (e.g., wireless transceiver 530) to transmit thedetermined information to the paired device to cause the paired deviceto provide the at least one alert. In one embodiment, apparatus 110 isconfigured to select which paired device should provide the at least onealert. To do so, apparatus 110 may determine the distance of each paireddevice from apparatus 110 and transmit the determined information to theselected paired device. For example, when user 110 is at his/her homeand not in proximity to his/her smartphone, apparatus 110 may select totransmit the determined information to a smarthome controller that willprovide a voice alert using associated speakers.

When the processing device is part of computing device 120 (e.g.,processor 540), step 3140 may include using feedback outputting unit 545or display 260 to provide the at least one alert to user 100. In someembodiments, the at least one alert may be associated with at least oneof a determined reading speed of user 100, a last page read of a book,or progress toward a predetermined reading goal. In addition, theprocessing device may record one or more reactions of user 100 to the atleast one alert, and to take into consideration past reactions to acertain type of alert, when determining if and how to provide futurealerts to user 100. For example, the processing device may determinethat user 100 ignores visual alerts presented on display 260 in somecases (e.g., while user 100 is in movement). Accordingly, the processingdevice may determine to use feedback outputting unit 545 whenidentifying contextual situations in these cases.

In another embodiment, after transmission of the determined informationin step 3140, apparatus 110 may continue to capture at least one imageusing image sensor 220. The processing device may analyze the at leastone image to identify a second contextual situation related to user 100.The second contextual situation may be related to the first identifiedcontextual situation (e.g., a repetitive of the same contextualsituation, a contextual situation that involves the same individuals, ora contextual situation that involves associated objects), or not relatedat all. The processing device may then determine a time differencebetween the first contextual situation and the second contextualsituation. Using the identified first contextual situation, theidentified second contextual situation, and the determined timedifference, the processing device may withhold transmission associatedwith the second contextual situation to computing device 120. Theprocessing device may withhold the transmission for an unlimited periodof time, a predetermined period of time, or until the next time theapparatus 110 is being charged. In one of the examples mentioned above,the first contextual situation may include an identification of a workerat a work site not wearing safety equipment. In this example, the secondcontextual situation may include another identification of the sameworker at the same work site still not wearing safety equipment. Theprocessing device, may determine to withhold the transmission ofinformation associated with the second contextual situation to computingdevice 120 if the determined time difference between the two times thatapparatus 110 identified that the worker is not wearing safety equipmentis under or above a predefined threshold. For example, the predefinedthreshold may be twelve hours, six hours, two hours, thirty minutes,five minutes, one minute, thirty seconds, etc.

Collaboration Facilitator for Wearable Devices

In some embodiments, a wearable apparatus 110 may capture image data(e.g., one or more images) of an environment of the wearer of thewearable apparatus 110. The image data may be analyzed to detect avisual trigger in the environment. The visual trigger may indicate thata collaborative action is to be taken. An indication based on the visualtrigger may then be transmitted to other wearable apparatuses 110,computing devices 120 that are paired with wearable apparatuses 110,and/or to server 250. Receipt of the indication of the visual triggermay cause an action by the receiving device, such as distributinginformation to other devices.

FIG. 32A illustrates an example environment 3200 including a user 3205wearing a wearable apparatus 110, computing devices 3210 and 3220, andmeeting attendees 3230. Environment 3200 may depict a meeting in whichuser 3205 is standing at a blackboard, video screen, or otherpresentation device 3207, and giving a presentation to a group ofmeeting attendees 3230. User 3205 may be wearing wearable apparatus 110,which may have a field of view (FOV) such that the wearable apparatus110 may capture images of at least part of presentation device 3207.Wearable apparatus 110 may also be used in environment 3200 to detect avisual trigger in the environment. In some examples, a systemindependent of wearable apparatus 110 may be used to capture images anddetect the visual trigger, or to detect the visual trigger in imagescaptured by wearable apparatus 110. For example, a smart phone, computerwith an attached camera, a digital camera, or other image capturingdevice may be used. By way of example, the following embodiments willdescribe the system with respect to wearable apparatus 110.

In some embodiments, wearable apparatus 110 (or other system) mayinclude a transceiver and at least one processing device, as describedabove for FIG. 5. The processing device may be coupled to memory storinginstructions, which when executed may cause the device to performoperations. In some embodiments the processing device may be programmedto cause the wearable apparatus 110 to capture images of the environmentof user 3205. The wearable apparatus 110 may include an image sensor, asdescribed above, to capture images. The processor may obtain thecaptured images from the image sensor. In some examples, the capturedimages may be of the presentation device 3207. For example, the FOV ofwearable apparatus 110 may be such that wearable apparatus 110 generally“sees” what user 3207 sees. In other examples, the captured images maybe of a wide angle view capturing an image with an FOV of, for example,270 degrees. The wide angle FOV may allow an image to be captured thatincludes meeting attendees or other persons or objects that may or maynot be visible to user 3205.

In some embodiments, environment 3200 may include instances where acollaborative action may be taken. For example, a collaborative actionmay be an action common to or shared amongst the wearer of wearableapparatus 110 and at least one other individual. In some examples, thecollaborative action may be a group work project, updating a projectstatus, treating a patient, updating a patient record, updating a tasklist, and/or assembling an item. For example, environment 3200 mayinclude a meeting with attendees involved in a group work project, suchas designing an automobile. Meeting attendees may work together onspecific facets of the project and share information with one anotherbased on visual triggers detected by wearable apparatus 110 whenanalyzing captured images of the environment. In other examples, thecollaborative action may be commercial in nature, such as selling aproduct, marketing a product, or advertising a product. Thecollaborative action may also be recreational, such as playing a game ordriving a car. In some instances, the collaborative action may bepreparing a meal, cleaning a residence, buying groceries, or assemblingfurniture.

As described above, wearable apparatus 110 may capture images inenvironment 3200. The processing device may analyze the images to detecta visual trigger in the images. As discussed, the visual trigger may beassociated with a collaborative action to be taken. Wearable apparatus110 may transmit an indicator of the detected visual trigger using thetransceiver to other devices. For example, the indicator may betransmitted to paired device 3210, which may be paired to wearableapparatus 110. In some examples, paired device 3210 may be any devicecapable of receiving information through a paired connection to wearableapparatus 110, such as a smartphone, a tablet, and/or a smart watch.Paired device 3210 may be worn by user 3205, such as on a belt, orintegrated into a belt. Alternatively, paired device 3210 may be held byuser 3205 or be located in the vicinity of user 3205 (e.g., be locatedin the same room). In other examples, paired device 3210 may be locatedin a remote location (e.g., in another room, building, city, etc.).

In some embodiments, the paired device 3210 may process the receivedindicator of the visual trigger and determine an action to be taken. Insome examples, the indicator and/or visual trigger may be a command toperform a part of the collaborative action. The action may be a functionassociated with the collaborative action. The function may includeactions that the device normally performs, such as display of data onthe device, or the function may include distributing information toother devices, such as remote devices 3220. Remote devices 3220 may alsobe a smartphone, a tablet, a smart watch, and/or a server. For example,paired device 3210 may receive an indicator that a visual trigger hasoccurred in environment 3200. Receipt of the indicator may cause paireddevice 3210 to distribute information to remote devices 3220. Theinformation may be related to the visual trigger and/or thecollaborative action (e.g., an image of the presentation device, anupdate to a task list, an indication that the meeting has finished,etc.). The information may include images, documents, spreadsheets, orother information useful in the collaborative action.

In some examples, the information may be distributed to multipledevices, as depicted in FIG. 32A. Remote devices 3220 may be used byattendees 3230. For example, each attendee 3230 may hold a remote device3220 and view the information on the device. The information received byremote devices may supplement and/or augment the presentation by user3205.

In some embodiments, the information may identify a step in a taskand/or the amount of time spent on a step of a task. For example, paireddevice 3210 may receive the indicator of the visual trigger, which mayinclude a swiping hand gesture indicating to display the next picture orpage of information regarding the task. Paired device 3210 may thendistribute information to the remote devices 3220 identifying thecurrent step, such as designing the interior of a car, and the nexttask, such as determining the color of the interior. The information mayalso include how long the group spent on the current task or step. Insome examples, the amount of time may be factored into a budget. Forexample, the budget may indicate how long certain tasks require and maybe used to properly budget future tasks. In some examples, the result ofthe current task may be distributed before progressing to the indicatednext task. In this case, attendees may be able to view a task list onremote devices 3220. The task list may show the current task, withamount of time spent on the task, and the next task to be completed. Thetask list may also display all tasks in the collaborative action,whether or not completed.

FIG. 32B illustrates an exemplary hand gesture 3208 as a visual triggerassociated with a collaborative action. For example, user 3205 may bewearing wearable apparatus 110 and presenting a design of a new car toan audience. At some point during the presentation, user 3205 may swipehis arm upward, for example, as in the gesture 3208 to indicate anaction. The action may indicate an end to a task, such as moving to thenext slide, signaling that a consensus has been reached, a decision hasbeen made, a task has been completed, an item has been bought, or thelike. In other examples, the gesture 3208 may indicate movement of theimage, such as moving it from one side of the presentation to the other.In still another example, the gesture 3208 may indicate that thedisplayed image be transmitted to attendees and/or information in adocument or spreadsheet (e.g., describing the image) be transmitted toattendees.

FIG. 32C illustrates and example environment 3201 in which informationis distributed to other wearable apparatuses 110 in response to a handgesture 3208. In the example, a user 3205 may be presenting to localmeeting participants (not shown), while also presenting to remoteindividuals 3240, who may be at a remote location. Additionally oralternatively, a user 3205 may be presenting to local meetingparticipants, and remote individuals 3240 may be involve in a differenttask associated with a collaborative action common to user 3205 andremote individuals 3240. Additionally or alternatively, a user 3205 maybe presenting to local meeting participants, where the meeting and/orthe presentation may be associated with a collaborative action common touser 3205 and individuals 3240, while remote individuals 3240 may beinvolved in a task that is unrelated to the collaborative action. Forexample, paired device 3210 may distribute (e.g., transmit) theinformation to other wearable apparatuses 110, which are worn byattendees (or other persons not in the environment). In some examples,the other wearable apparatuses 110 may be paired with other devices andtransmit information based on the received information to those paireddevices. By way of example, environment 3201 depicts a user 3205presenting a car design to meeting attendees (as shown in FIG. 32A).User 3205 may be wearing a wearable apparatus 110, which is paired toanother device 3210, such a smartphone. User 3205 may point to thepresentation and make a gesture 3208, which may cause the displayedimage to move. Wearable apparatus 110 may capture an image of thepresentation and the gesture, analyze the captured image, and determinethat a visual trigger has occurred (e.g., the gesture 3208). Wearableapparatus 110 may transmit an indicator of the visual trigger to thepaired device 3210. Wearable apparatus 110 may also transmit the imageor other information to the paired device 3210. The paired device 3210may receive the indicator and/or other information, which may cause thepaired device 3210 to distribute information (e.g., a received image, anindicator of the slide in a presentation, steps in a task, etc.) toother wearable apparatuses 110. The other wearable apparatuses may beworn by attendees 3230 (not shown) or by remote individuals 3240. Theother wearable apparatuses 110 may also be paired to devices, such asdisplay device 3209. In some examples, one or more of the other wearableapparatuses 110 may transmit the received information to the displaydevice 3209 for display to the remote individuals 3240. In someembodiments, other wearable apparatuses 110 worn by remote individuals3240 may also capture images and detect visual triggers at the remotelocation. For example, one of the remote individuals 3240 may gesture tothe display device 3209, which gesture may be detected as a visualtrigger by one of the other wearable apparatuses 110. In response todetecting the visual trigger, feedback may be sent to either paireddevice 3210 or wearable device 110 being worn by user 3205.

FIG. 33A illustrates an exemplary visual trigger associated with anenvironment 3300. In the embodiment, a physician 3320 may enter anexamination room where a patient 3315 is waiting. Physician 3320 may bewearing wearable apparatus 110. In some examples, wearable apparatus 110may capture images depicting a transition of the physician 3320 from thelobby to the examination room (e.g., changing the environment of thephysician 3320). In some examples, wearable apparatus 110 may captureimages depicting a meeting with patient 3315. The transition and/ormeeting may be detected as a visual trigger. An indicator of the visualtrigger may, for example, be transmitted to a paired device, a remotedevice, a server, and/or other devices, to indicate, for example, thatthe physician 3320 is meeting with the patient 3315. The transmittedindicator may cause the paired device, for example, to update a tasklist associated with the patient 3315. In another example, the wearableapparatus 110 may capture an image of the patient 3315 as the physician3320 enters the room. Wearable apparatus 110 may analyze the capturedimage and determine that the patient 3315 is present. The presence ofthe patient 3315 may cause wearable apparatus 110 to detect a visualtrigger that the physician 3320 will examine and/or speak with thepatient 3315. In some examples, an indication of the visual trigger maybe transmitted to another device, where it may cause the device toperform a function related to the collaboration (e.g., update a tasklist, provide treatment options for the patient, update a patientrecord, etc.). For example, the indication of the visual trigger may betransmitted to a server 250, which includes a database of records forpatient 3315. Receipt of the indication may cause the server 250 toupdate the records to indicate that the patient 3315 has arrived for herappointment. In another example, receipt of the indication may causeserver 250 (or other device) to distribute to a paired device used byphysician 3320 information related to patient 3315, such as a list ofailments that patient 3315 experiences, a treatment plan for patient3315, test results for patient 3315, and so forth.

FIG. 33B illustrates another exemplary visual trigger associated with anenvironment 3301. In the environment, user 3205 may be presenting at ameeting with remote individuals 3240 at a remote location and a newmeeting participant, person 3330, may join the meeting. Alternatively,person 3330 may leave the meeting. In response to joining the meeting,person 3330 may receive information about the presentation, such astasks associated with the meeting, the duration of the meeting,instructions pertaining to participation in the meeting, etc. In someembodiments, user 3205 may be wearing wearable apparatus 110, which mayinclude an image sensor with a field of view that includes a door to theroom. In some embodiments, wearable apparatus 110 may capture an imageof a person 3330 entering (or exiting) the room where the presentationis taking place. The image may be analyzed to detect a visual triggerthat the person 3330 has entered (or exited) the room and an indicatorof the visual trigger may be transmitted to a remote device. Forexample, the indicator may be transmitted to a device being used byperson 3330. The indicator may cause the presentation to be displayed onthe device and/or an indication of the current task or step in a taskmay be displayed. In another example, the indicator may be transmittedto remote attendees 3240.

FIG. 33C illustrates an exemplary environment 3302 including a user 3205wearing a wearable apparatus 110, an audience 3340, a network 240, and aserver 250. In some embodiments, each audience member may also have awearable apparatus 110. Each wearable apparatus 110 may acquireimage-based information associated with images captured by the camera onthe particular wearable apparatus. Image-based information may includeraw images captured from the camera and formatted as jpeg, pic, tiff,mpeg, or any other suitable image format. Additionally or alternatively,image-based information may include images pre-processed by a processingdevice on the wearable apparatus 110. The pre-processed images may becategorized, enhanced, compressed, or otherwise altered. Additionally oralternatively, image-based information may include logos, words and/orfacial information extracted from the images. Additionally oralternatively, image-based information may include information relatedto products, people, etc. that the users interact with, such asidentifying information, names, and so forth. The image-basedinformation may be of the situation or environment 3302 of the user 3205wearing the wearable apparatus 110. In some embodiments, each wearableapparatus 110 may transmit image-based information to server 250. Insome examples, wearable apparatuses 110 may first transmit theinformation to a network 240 and then to server 250. In other examples,the wearable apparatuses 110 may transmit the information directly toserver 250.

In some embodiments, the image-based information is streamed to server250 in a data stream. As discussed above in connection with FIG. 27B,the data stream may consist of packet or frames that are transmitted byany suitable transmission means. The packets may include image-basedinformation, position information, timing information, motioninformation, and so forth. The data stream (e.g., as shown in FIG. 27B)may occur in real-time (e.g., shortly after the image data is acquired,for example within one second), or the stream may be delayed by apredetermined amount of time. Thus, server 250 may receive one or moredata streams of image-based information from each wearable apparatus110. In some examples, server 250 may also receive one or more datastreams of image-based information from one or more wearable apparatuses110 used by individuals not present in environment 3302.

In some embodiments, server 250 may include a data interface (not shown)that allows server 250 to receive one or more data streams from wearableapparatus(es) 110. The data interface may include hardware and/orsoftware to interface with network 240 or directly to wearableapparatuses 110. For example, the data interface may includetransmitting and receiving circuitry for a wired or wireless connectionto network 240. Server 250 may also include a processing deviceoperatively coupled to memory for storing instruction for the processingdevice.

In some embodiments, devices other than or in addition to server 250 mayreceive the transmitted data streams from wearable apparatus 110. Forexample, computing device 120 may receive the data streams.Alternatively, any one or all of the wearable apparatuses 110 mayreceive the data streams from the other wearable apparatuses 110.

In some examples, server 250 may analyze the image-based information ineach of the received data streams. For example, server 250 may receive adata stream from a particular wearable apparatus 110 using the datainterface. Server 250 may, using the processing device, unpack orextract the image-based information in the data stream. In someexamples, the image-based information may be a series of images capturedby the camera on the particular wearable apparatus 110. In otherexamples, the image-based information may include camera settings, suchas f-stop, focal length, light and color content of the image, etc. Inother examples, the image-based information may include an indication ofat least one visual trigger, for example, an indication of a visualtrigger associated with a collaborative action. In other examples, theimage-based information may include information related to persons,objects, events, actions, etc. captured by wearable apparatus 110.

Server 250 may analyze the image-based information to determine at leastone visual trigger associated with a collaborative action. In someexamples, server 250 may use image recognition algorithms and/or machinevision algorithms to determine objects and/or persons in the receivedimages. For example, optical character recognition (OCR) may be used todetermine words in an image, such as on a paper, sign, book, etc.Detected words may be used to recognize consumer products, brand names,and/or categories of products. In some examples, edge and shaperecognition may be used to determine objects in images, such as a ball,a tree, lines on a playing field, etc. Facial recognition may be used todetermine features on a human face, where the features may be comparedto a database of features to determine the identity of a person. In someexamples, context analysis may be used to determine situations involvingthe recognized words, object, people, etc. For example, an image may becaptured and analyzed to determine the presence of a ball, grass, lineson the grass, and a soccer player. Contextual analysis may thendetermine that the user is attending a soccer game. Other non-exhaustiveexamples of context analysis include: water+boat=boating;grass+ball=sport (the type of ball may also be recognized to determineif the sport is, for example, soccer, baseball, football, etc.);recognize lines of a playing field; aisles+products=grocery store; readtext on object to recognize brands of products (e.g., Colgate, etc.);recognize general descriptive words (e.g., potatoes, milk, etc.), forexample, at a produce market.

Furthermore, in some embodiments, for example, analyzing images mayinvolve edge identification, in which an image is analyzed to detectpixels at which discontinuities (e.g., sudden changes in imagebrightness) occur and edges (e.g., edges of the external object) areidentified to coincide with the detected pixels. Alternatively oradditionally, in some embodiments analyzing images may involveidentifying in and/or extracting from an image pixels representative ofobjects in the environment, such as the external object. Pixels may bedetermined to be representative of an external object based on, forexample, other images of the external device or similar external devicesmaintained, e.g., in a database and/or predetermined data describing theexternal object maintained, e.g., in a database. Alternatively oradditionally, pixels may be determined to be representative of anexternal object based on, for example, a trained neural networkconfigured to detect predetermined external objects. Other types ofanalysis are possible as well, including, but not limited to, gradientmatching, greyscale matching, scale-invariant feature transform (SIFT)matching, and/or interpretation trees. In the case where multiple imagesare received over a period of time, server 250 may compare sequentialimages to determine actors and actions taking place in the images.

In some examples, the collaborative action includes distributing (e.g.,transmitting) information to other devices. The server may transmit theinformation through network 240 to, for example, wearable apparatuses110, or to other devices, such as remote devices 3220 or computingdevice 120 (e.g., being used by audience 3340). The information may beas described above and distributed to other devices based on the visualtrigger.

By way of example, wearable apparatus 110 being worn by user 3205 maystream image-based information to server 250 for analysis. Server 250may detect one or more visual triggers in the image-based data. Forexample, the visual trigger may be a hand gesture such as pointing to aparticular aspect of the presentation. Server 250 may interpret thegesture as a command to distribute a questionnaire, a task guide, orother interactive information to audience members 3340. Server 250 maytransmit the information through network 240 to wearable apparatusesbeing worn by audience members 3340 or alternatively to computingdevices being used by audience members 3340. Audience members 3340 maythen provide feedback to user 3205, such as real-time results from asurvey.

In some embodiments, server 250 may receive two or more data streamsfrom different wearable devices. Server 250 may analyze the two or moredata streams and determine multiple visual triggers. In someembodiments, server 250 may determine at least one visual trigger basedon the two or more data streams being analyzed in parallel. For example,images from difference data streams may contain information that whenanalyzed together cause the server to detect a visual trigger. Forexample, server 250 may determine that two or more users of wearableapparatuses 110 are working on the same step of a collaborative actionand performing redundant actions, such as buying the same item, editingthe same document, processing the same information, and so forth. Inanother example, server 250 may determine that a need of a first user ofa first wearable apparatus 110 may be fulfilled by a second user of asecond wearable apparatus 110. For example, that the first user islacking an item that the second user has excess of, that the location ofthe second user enables the second user to assist the first user, and soforth.

In some embodiments, server 250 may store the information relating tothe determined visual trigger in a storage resource 3350. The storageresource 3350 may be local to server 250 or at a remote location. Insome examples, the storage resource 3350 may be internal memory ofserver 250 and/or computing device 120. Storage resource 3350 may alsobe memory within any one of wearable apparatuses 110. In some examples,storage resource 3350 may be a nonvolatile storage medium, such as ahard disc or a solid state disc. Server 250 may store the informationrelating to the determined visual trigger in a database on storageresource 3350. The database may be of any known kind, such as arelational database or a self-referencing database. The database mayinclude demographic information about the user or users associated withthe visual triggers. For example, a database entry containinginformation relating to the determined visual trigger may also containbiographical information (e.g., demographics) about the user or usersassociated with the trigger.

In some embodiments, the stored database information and/or informationbased on the stored database information may be output to a user,wearable apparatus 110, remote device, or other computing device 120.For example, server 250 may include a monitor 3360 and display agraphical user interface (GUI) containing the information relating tothe visual trigger or associated collaborative action. In otherexamples, the stored information may be displayed on computing device120. In still other examples, the stored information may be presented tothe user through a display on a wearable apparatus 110 or paired device3210. In some embodiments, the stored information may be communicated toa user audibly. The displayed or otherwise communicated information mayinclude the determined visual trigger, collaborative action, and/ordemographics information about the users of the wearable apparatuses towhich the visual trigger or collaborative action belongs.

FIG. 33D illustrates another exemplary visual trigger associated with anenvironment 3303. In the environment, a user 3370 may be wearingwearable apparatus 110 and working at a computer 3380. One of ordinaryskill in the art will recognize that computer 3380 may include anycomputing device, such as a laptop, desktop computer and associatedmonitor, or a handheld device (e.g., a smartphone, tablet, smart watch,etc.)

In the example shown in FIG. 3D, user 3370 may be entering informationinto a graphical user interface (GUI) 3390, such as a spreadsheet orword processing program. Wearable apparatus 110 may detect that the user3370 is entering the information and determine that entering theinformation is a visual trigger. In some examples, wearable apparatus110 may capture images of the GUI 3390 and determine which program isdisplayed on the screen. For example, wearable apparatus 110 may detecta logo present on the GUI 3390, the arrangement items on the screen, orother indicators of the type of program. Wearable apparatus 110 may alsoanalyze the captured images for changes due to inputting of informationby user 3370. Identification of the GUI 3390 and the inputting ofinformation may cause detection of a visual trigger. An indication ofthe visual trigger may be sent to a paired device, or alternatively, tocomputer 3380. Receipt of the indication may cause, for example,computer 3380 to retrieve data from a database and input the informationinto the GUI 3390 (e.g., in the case that the user 3340 is entering taxreturn information, etc.). In other examples, the user 3370 may becompleting a checklist by indicating that tasks associated with acollaborative action have been completed. Completion of the checklistmay be detected as a visual trigger and an indication may be sent to apaired device, or other computing device. Receipt of the indication maycause the paired device to distribute information to other individualsinvolved with the collaborative action to inform them of its completion.

FIG. 34 illustrates an exemplary embodiment of memory 3440 containingsoftware modules to determine a visual trigger. For example, one or moreof server 250, computing device 120, or wearable apparatus 110 mayexecute instructions from the modules to perform one or more of thefunctions as described with respect to FIGS. 32-33, above. Included inthe memory are receiving module 3441, analysis module 3442, andtransmitting module 3443. Modules 3441, 3442, and 3443 may containsoftware instructions for execution by at least one processing deviceincluded with a server-based system, such as is included in server 250,a wearable apparatus 110, or other computing device.

Receiving module 3441 may be configured to receive one or more datastreams or image-based data transmitted by wearable apparatuses.Receiving module 3441 may interact with the data interface to receivethe one or more data streams. Receiving module 3441 may control the datainterface to receive multiple data streams simultaneously from one ormore transmission sources. For example, data streams may be receivedthrough a wired connection or through a wireless connection or throughboth.

Analysis module 3442 may be configured to analyze the one or morereceived data streams or image-based data to determine one or morevisual triggers associated with collaborative action to be taken withrespect to a user in an environment. Analysis module 3442 may extractinformation from the data streams to aid in determination of the visualtrigger. For example, analysis module 3442 may extract image-basedinformation, position information, timing information, and/or motioninformation from the data streams. Analysis module 3442 may use facialdetection and recognition to determine triggers, such as interactionwith other persons. Analysis module 3442 may also use machine visionalgorithms to determine triggers such as identifying commercialproducts, landscape, objects, locations, gestures, assembly of items,entry of data, etc. Furthermore, analysis module 3442 may use positioninformation to determine a location of the trigger, timing informationto determine frequency, scheduling, and/or duration of the trigger, andmotion information to determine specific interaction states, such asmovement towards an object.

Transmitting module 3443 may be configured to distribute informationrelated to the collaborative action to be taken. Transmitting module3443 may interact with the data interface to transmit the information tonetwork 240, wearable apparatus 110, paired device 3210, and/orcomputing device 120. Transmitting module 3443 may control the datainterface to transmit information to multiple devices simultaneously.

FIG. 35 is a flowchart illustrating an exemplary method 3500 ofdetecting a visual trigger and transmitting an indicator relating to thevisual trigger consistent with disclosed embodiments.

At step 3505, one or more images may be obtained. The images may becaptured by wearable apparatus 110. In some embodiments, the images maybe obtained by a processing device from an image sensor. In otherembodiments, the images may be obtained from data streams transmitted bywearable apparatus 110 to a remote computing device 120 and/or server250. The one or more data streams may contain image-based information,position information, timing information, and/or motion information, andbe sent in packets or frames. The data streams may be continuouslyreceived over time or received at predetermined intervals.

In some embodiments, the images may be of the environment of a user ofwearable apparatus 110. For example, the images may capture actions ofother persons, hand gestures of the user, and/or objects and features ofthe user's environment. As described above, images may be capturedduring a meeting presentation, a doctor visit, during a sporting event,while assembling an object, etc.

At step 3510, the one or images may be analyzed to detect at least onevisual trigger. In some examples, the visual trigger is determined fromtwo or more users from which the images are received. In someembodiments, server 250 may analyze the images. In other embodiments,computing device 120 or wearable apparatus 110 may analyze the images.For example, the images may be analyzed by executing instructions storedin analysis module 3442. In some embodiments, the images may be analyzedto determine a visual trigger associated with a collaborative action tobe taken (as described above). In some embodiments, positioninformation, timing information, and/or motion information may beanalyzed in addition to image-based information in order to determine avisual trigger. For example, image-based information may be analyzedusing machine vision, image recognition, OCR, etc. algorithms, asdescribed above, to determine features and contexts within images orsets of images, such as a hand gesture or recognize another person. Insome examples, timing and motion information may be used to determine ahand gesture. Thus, a visual trigger may be determined relating to themotion of a user's hand. In other examples, objects, logos, data typesbeing input into a GUI, interactions with people or object, etc. may bedetermined.

At step 3515, an indicator related to the determined visual trigger maybe transmitted. The indicator may represent the visual trigger and causefunctions or actions to be performed by the receiving device. Forexample, the visual trigger (through the indicator) may be recognized asa command to perform a collaborative action or distribute information.As described above, a device receiving the indicator of the visualtrigger may distribute task lists, timing information related to taskduration, interactive surveys, etc., to other devices used by otherindividuals.

Privacy Mode for a Wearable Device

In some embodiments, a wearable apparatus may enter a privacy mode incertain situations and send substitute images (e.g., a cartoon versionof an image) or censored images (e.g., blurred) when a private situationis detected. In some embodiments, the wearable apparatus may store inmemory indicators of a plurality of private contextual situations andthose indicators may be transmitted to a paired device so that imagesfrom that time period are censored, not shared, or both. In cases wherethe wearable apparatus may be configured to remain on throughout theday, this feature may protect user privacy.

FIG. 36A is a block diagram illustrating components of wearableapparatus 110 according to an example embodiment. FIG. 36A is similar toFIG. 5A and includes all the features of FIG. 5A, with the onlydifference being the addition of an audio sensor 3610 depicted inwearable apparatus 110. Adding audio sensor 3610 in FIG. 36A is onlyincluded for ease of description and reference. It does not imply thatthe system shown in FIG. 5A does not include such an audio sensor orthat audio sensor 3610 is a necessary component in the system shown inFIG. 5A. As described above in connection with FIG. 5A, other sensorsincluding a microphone (e.g., an example of audio sensor 3610) may beincluded in wearable apparatus 110. On the other hand, some embodimentsmay omit audio sensor 3610.

FIG. 36B is a block diagram illustrating components of wearableapparatus 110 according to an example embodiment. FIG. 36B is similar toFIG. 5B and includes all the features of FIG. 5B, with the onlydifference being the addition of audio sensor 3610 in the figure. Again,adding audio sensor 3610 in FIG. 36B is only included for ease ofdescription and reference. It does not imply that the system shown inFIG. 5B does not include such an audio sensor or that audio sensor 3610is a necessary component in the system shown in FIG. 36B.

FIG. 36C is a block diagram illustrating components of wearableapparatus 110 according to an example embodiment. FIG. 36C is similar toFIG. 5C and includes all the features of FIG. 5C, with the onlydifference being the addition of audio sensor 3610 in the figure. Again,adding audio sensor 3610 in FIG. 36C is only included for ease ofdescription and reference. It does not imply that the system shown inFIG. 5C does not include such an audio sensor or that audio sensor 3610is a necessary component in the system shown in FIG. 36C.

For any of the embodiments depicted in FIGS. 36A-36C, system 200 may beconfigured to automatically change settings and configuration ofwearable apparatus 110 based on analysis of information collected bywearable apparatus 110. As described above, wearable apparatus 110 maybe worn by user 100 in various ways. Wearable apparatus 110 may collectdata in the environment of user 100, such as capturing images, recordingsound, etc. The collected data, which may or may not be preprocessed bywearable apparatus 110, may be transmitted to computing device 120,which may be paired with wearable apparatus 110 through a wired orwireless communication link. Computing device 120 may analyze thereceived data, alone or in combination with server 250 through network240, may store predefined privacy mode triggers and associated privacymode settings in memory 550/550 a/550 b, may determine informationassociated with the analyzed data indicating the presence of at leastone of the predefined privacy mode triggers, and/or may automaticallycause one or more adjustments to the wearable imaging apparatus based onthe privacy mode settings associated with the at least one recognizedpredefined privacy mode trigger.

Referring to FIG. 36C, wearable apparatus 110 may establish wirelesscommunication (also referred to as wireless pairing) with computingdevice 120 (also referred to as a paired device or an external device).As described above, computing device 120 may include one or moresmartphones, one or more tablets, one or more smartwatches, one or morepersonal computers, one or more wearable devices, a combination thereof,and so forth. In some embodiments, wearable apparatus 110 and computingdevice 120 may be paired through a short range communication link suchas Bluetooth, WiFi, near-field communication (NFC), etc. In someembodiments, wearable apparatus 110 may be connected to network 240 andcommunicate with computing device 120 and/or server 250 through network240. For example, wireless pairing may be established via communicationbetween wireless transceiver 530 a in wearable apparatus 110 andwireless transceiver 530 b in computing device 120. Wireless transceiver530 a may act as a transmitter to send image and/or sound data capturedby image sensor 220 and/or audio sensor 3610 to wireless transceiver 530b, which may act as a receiver, for processing and analysis by computingdevice 120, alone or in combination with server 250. In someembodiments, wireless transceiver 530 a may transmit informationassociated with the captured image/sound data to computing device 120.In some embodiments, after a content item is selected by computingdevice 120, the selected content item may be transmitted by wirelesstransceiver 530 b, which may act as a transmitter, to wirelesstransceiver 530 a, which may act as a receiver. The content item maythen be output to user 100 through feedback outputting unit 230.

In some embodiments, wearable apparatus 110 may be configured to collectdata (e.g., image and/or audio data) and transmit the collected data tocomputing device 120 and/or server 250 without preprocessing the data.For example, processor 210 may control image sensor 220 to capture aplurality of images and/or control audio sensor 3610 to record sound.Then, processor 210 may control wireless transceiver 530 a to transmitthe captured images/sound data, and/or information associated with theimages/sound, to computing device 120 and/or server 250 for analysiswithout performing preprocessing or analysis using the computationalpower of processor 210. In some embodiments, processor 210 may performlimited preprocessing on the collected data, such as identifying apredefined privacy mode trigger in the images or in the sound data,performing optical character recognition (OCR), compressing theimage/sound data, sampling the image/sound data, identifying userbehavior related images/sound, etc. The preprocessed data may then betransmitted to computing device 120 and/or server 250 for furtheranalysis. In some embodiments, processor 210 may perform the entirety ofthe privacy mode trigger detection and privacy mode settings adjustmentprocesses (as will be discussed in further detail below).

In some embodiments, computing device 120 may be configured to performsome or all of the privacy mode trigger detection and privacy modesettings adjustment processes. For example, wearable apparatus 110 maytransmit image/sound data, either unprocessed or preprocessed, tocomputing device 120. After receiving the data, processor 540 mayanalyze the data to identify, for example, one or more images or soundsfrom the environment of user 100. Processor 540 may then determine,based on the analysis, information associated with the one or moreimages or sounds. The information may include a scene, a person, anobject, a trigger, etc. that is included in the image(s) or sound(s).The information may also include the time and/or location of capturingthe image(s) or sound(s). The information may also include historicaldata relating to the scene, person, object, trigger, etc. depicted inthe image(s) or sound(s). Other suitable information relating to theimage(s) or sound(s) may also be determined.

As described above, one or more privacy mode trigger detection andprivacy mode settings adjustment processes may also be performed byserver 250 and/or wearable apparatus 110. Tasks such as image/sound dataanalysis, information determination, and content selection, may bedivided among wearable apparatus 110, computing device 120, and server250 in any suitable manner. In some embodiments, two or more devices(110, 120, and/or 250) may also collaboratively perform any one task orprocess. For example, wearable apparatus 110 may preprocess the capturedimage/sound data, select a plurality of images/sounds from theenvironment of user 100, and transmit the plurality of images/sounds tocomputing device 120.

Computing device 120 may analyze the plurality of images/sounds toidentify one or more images/sounds relevant to one or more privacy modetrigger detection and privacy mode settings adjustment processes, andtransmit the identified one or more images to server 250. Server 250 mayperform one or more tasks related to one or more privacy mode triggerdetection and privacy mode settings adjustment processes, and transmitinformation and/or feedback to computing device 120 and/or wearableapparatus 110.

There are various ways of distributing and dividing tasks or subtasksamong wearable apparatus 110, computing device 120, and server 250.Regardless of which task or subtask is performed by which device, anysuitable allocation of computation resources among the devices forperforming the above described tasks and/or processes are within thepurview of the present application.

FIG. 37 illustrates exemplary software modules contained in one or morememory units, such as memory 550, memory 550 a, and/or memory 550 b. Asshown in FIG. 37, the exemplary software modules include an imageanalysis module 3710, an audio analysis module 3720, an image alterationmodule 3730, an action execution module 3740, a database access module3750, and one or more databases 3760. As described above, computationaltasks involved in system 200 relevant to one or more privacy modetrigger detection and privacy mode settings adjustment processes may beallocated among wearable apparatus 110, computing device 120, and server250. Therefore, software modules shown in FIG. 37, which arefunctionally similar to the computational tasks, are not necessarilystored in a single memory unit. Rather, the software modules can beallocated, similar to the computational tasks, among the various deviceshaving computational power in system 200. For example, memory 550 a maycontain modules 3710 and/or 3720, while memory 550 b may contain modules3730, 3740, and 3750, as well as database(s) 3760. In anotherembodiment, all modules shown in FIG. 37 may be contained in memory 550b. In yet another embodiment, all modules shown in FIG. 37 may becontained in memory 550 (as shown in FIGS. 5A and 5B and/or 36A and36B). In some embodiments, multiple memory units, for example memories550 a and 550 b, may both contain certain modules, such as modules 3710and 3720, and the computational tasks of image/sound analysis may bedynamically allocated or shifted between wearable apparatus 110 andcomputing device 120, depending on their respective work load.Therefore, the memory unit shown in FIG. 37 is collectively referred toas 550/550 a/550 b, indicating that the software modules shown in FIG.37 may or may not be contained in a single memory unit.

Similar to modules 601, 602, and 603 shown in FIG. 6, the softwaremodules shown in FIG. 37 may contain software instructions for executionby at least one processing device, e.g., processor 210 and/or processor540. Image analysis module 3710, audio analysis module 3720, imagealteration module 3730, action execution module 3740, and databaseaccess module 3750 may cooperate to perform one or more privacy modetrigger detection and privacy mode settings adjustment processes.

In some embodiments, image analysis module 3710 may contain softwareinstructions for analyzing one or more images and/or for performingoptical character recognition (OCR) of at least one image captured byimage sensor 220. For example, referring to FIG. 36C, processor 210 mayexecute the image analysis module 3710 stored in memory 550 a to performanalysis of one or more images captured by image sensor 220, andtransmit a result of the analysis to computing device 120 via wirelesstransceiver 530 a. Processor 540 of computing device 120 may beprogrammed to receive the result via wireless transceiver 530 b.

As discussed above, image analysis module 3710 upon execution byprocessor 210/540, may enable processor 210/540 to process the capturedimage data and identify elements of images and/or textual informationwithin the captured image data. In certain aspects, textual informationconsistent with the disclosed embodiments may include, but is notlimited to, printed text (e.g., text disposed on a page of a newspaper,magazine, book), handwritten text, coded text, text displayed to a userthrough a display unit of a corresponding device (e.g., an electronicbook, a television a web page, or an screen of a mobile application),text disposed on a flat or curved surface of an object within afield-of-view of apparatus 110 (e.g., a billboard sign, a street sign,text displayed on product packaging), text projected onto acorresponding screen (e.g., during presentation of a movie at atheater), and any additional or alternate text disposed within imagescaptured by image sensor 220. Image analysis module 3710 may providefunctionality for apparatus 110 to analyze sets of real-time image datacaptured by image sensor 220. Processor 210/540 may execute imageanalysis module 3710, for example, to determine the presence of a visualtrigger in one or more sets of image data, to determine the presence ofspecific events and/or actions in one or more sets of image data, todetermine the presence of objects in one or more sets of image data, todetermine the positions of objects in one or more sets of image dataover time, and to determine the relative motion of those objects.

Furthermore, in some embodiments, for example, analyzing one or moreimages may involve edge identification, in which an image is analyzed todetect pixels at which discontinuities (e.g., sudden changes in imagebrightness) occur and edges (e.g., edges of a device, a body part of theuser, and/or an object associated with the user) are identified tocoincide with the detected pixels. Alternatively or additionally, insome embodiments analyzing one or more images may involve identifying inand/or extracting from an image pixels representative of one or moreobjects, actions, events, and/or visual triggers in the environment,such as an object, a body part of the user, and/or an object associatedwith the user. Pixels may be determined to be representative of anobject, an action, an event, and/or a visual trigger based on, forexample, other images of the object maintained, e.g., in a databaseand/or predetermined data describing the object, action, event, and/orvisual trigger maintained, e.g., in a database (e.g., other images ofthe device, of the body part of the user, and/or of the deviceassociated with the user). Alternatively or additionally, pixels may bedetermined to be representative of an object, an action, an event,and/or a visual trigger based on, for example, a trained neural networkconfigured to detect predetermined objects, actions, events, and/orvisual triggers (e.g., predetermined devices, body parts of the user,and/or devices associated with the user). Other types of analysis arepossible as well, including, but not limited to, gradient matching,greyscale matching, scale-invariant feature transform (SIFT) matching,and/or interpretation trees.

In some embodiments, audio analysis module 3720 may contain softwareinstructions for analyzing sound recorded by audio sensor 3610. Forexample, audio sensor 3610 may record sound continuously and store therecorded sound data in memory 550/550 a/550 b. Memory 550/550 a/550 bmay store the sound data in a buffer, which may have a size sufficientfor storing a predetermined length of sound, such as 5 seconds, 10seconds, 30 seconds, 60 seconds, etc. Sound data stored in memory550/550 a/550 b may be transmitted to computing device 120, for exampleafter a privacy mode trigger is recognized in at least one of thecaptured images. For example, processor 210/540 may receive and analyzethe images captured by image sensor 220 to recognize a privacy modetrigger, such as a hand gesture, a person, an object, a location, ascene, etc. After the privacy mode trigger is recognized, processor 210may transmit the sound data stored in memory 550/550 a/550 b tocomputing device 120. Processor 210 may also transmit sound datarecorded after the recognition of the trigger to computing device 120,for example, for a designated time period (e.g., 5 seconds, 10 seconds,30 seconds, 60 seconds, etc.). After receiving the sound data, processor540 may execute software instructions of audio analysis module 3720, forexample, to extract information from the sound data recorded beforeand/or after the recognition of the trigger.

In some embodiments, other components of system 200 may perform tasks orfunctions based on either or both analysis results of modules 3710 and3720. For example, actions may be executed by action execution module3740 based on image(s) and/or OCR result without sound information. Inanother example, action execution may be based on sound informationalone, such as identifying a person's name, an object, a place, a date,a time point, or other information from the sound. In another example,action execution may be based on both images and sound. As describedabove, a privacy mode trigger can be identified from an OCR result ofone or more images captured by image sensor 220. Based on the trigger,sound data may be analyzed by audio analysis module 3720.

Image alteration module 3730 may provide functionality for wearableapparatus 110 to alter image data captured by image sensor 220 inresponse to detection of a privacy mode trigger by wearable apparatus110. Image alteration module 3730 may be configured to alter the imagedata in response to a command from action execution module 3740,database access module 3750, or both. In other embodiments, imagealteration module 3730 may be configured to alter the image dataindependent of modules 3740-3750. Alterations performable by imagealteration module 3730 include, but are not limited to, placinglimitations on image data that may be transmitted to a mobile device(such as computing system 120); altering the image data to enablecapturing but not storing of the images; prohibiting transmission ofcaptured image data; lowering the resolution of captured image data;distorting the captured image data; blurring the captured image data;enabling transmission of image data to another device after alterationssuch as those discussed above have been executed; enabling transmissionof caricature or cartoon representations of captured image data;altering the captured image data in a manner prohibiting that image datafrom being posted on social media; applying an image filter to imagedata captured by image sensor 220; and applying a convolution modelfilter to image data captured by image sensor 220.

Image alteration module 3730 may be configured to process and alterimages using one or more processing schemes based on the privacy modetrigger and the associated privacy mode settings, which will bediscussed in further detail below. In some embodiments, processor210/540 may be programmed to identify multiple portions of a singleimage and perform processing actions on each portion independently. Forexample, based on the detected presence in the captured image data ofone or more predefined privacy mode triggers, image alteration module3730 may identify a first and a second portion of at least one image ofthe plurality of images, and may process the first portion using a firstprocessing scheme, and process the second portion using a secondprocessing scheme. Each of the first, second, or more processing schemesmay incorporate one or more of the alterations discussed above, or otheralterations.

Action execution module 3740 may provide functionality for wearableapparatus 110 to execute various functions in response to stimuli, bethey privacy mode triggers detected by apparatus 110, appearance ofobjects or sounds within the vicinity of apparatus 110, or other eventsoccurring while apparatus 110 is in operation. Action execution module3740 may, for example, coordinate the configuration and execution of oneor more alternative actions that may be available to wearable apparatus110 upon positive identification of an object, an event, an action, atrigger, a sound, and/or a particular situation. These alternativeactions may include, but not be limited to, suspending image capture byimage sensor 220; suspending storage of images captured by image sensor220; limiting information transmitted to a paired device (such ascomputing system 120); prohibiting all transmission of images capturedby image sensor 220; enabling transmission of images that have passedthrough image alteration module 3730; suspending capture of audioinformation from audio sensor 3610; suspending transmission of audioinformation captured from audio sensor 3610; prohibiting posting ofimages captured by image sensor 220 on social media; causingtransmission to a paired device (such as computing system 120) ofinformation indicative of the identity of an individual detected in oneor more images captured by image sensor 220, without transmitting thoseimages to the paired device, to thereby enable the paired device toexecute a function relating to the individual without receiving the oneor more images; displaying information relating to an individualdetected in one or more images captured by image sensor 220; andblocking at least part of an application program interface (“API”)functionality.

Database access module 3750 may provide functionality for wearableapparatus 110 to compare objects, actions, events, visual triggers,and/or sounds detected in the user environment to objects, actions,events, visual triggers, sounds, and/or categories of same in adatabase, such as database(s) 3760, to be described in detail below. Insome embodiments, database access module 3750 may derive informationfrom real time image data received from image sensor 220 or audio sensor3610. In other embodiments, other software elements or processors mayderive the information and provide it to database access module 3750.Processor 210/540 may execute database access module 3750 to access oneor more of the described databases, and compare the information derivedfrom the received real time image/sound data with information in thedatabases. If the derived information corresponds to information foundin one or more of the databases, database access module 3750 may providean indication to action execution module 3740 to that effect asdiscussed in further detail below in association with FIG. 39.

Database(s) 3760 may comprise one or more databases that storeinformation and are accessed and/or managed through memory 550/550 a/550b. By way of example, database(s) 3760 may include document managementsystems, Microsoft™ SQL databases, SharePoint™ databases, Oracle™databases, Sybase™ databases, files, data structures, container datastructures, and/or other relational databases or non-relationaldatabases, such as Hadoop sequence files, HBase, or Cassandra. Thedatabases or other files may include, for example, data and informationrelated to the source and destination of a network request, the datacontained in the request, etc. Systems and methods of disclosedembodiments, however, are not limited to separate databases. Database(s)3760 may contain software code or macros that facilitate rapid searchingand comparison by database access module 3750. In some embodiments,database(s) 3760 may be configured to store one or more predeterminedprivacy mode triggers, or relevant information thereto. The storedinformation may comprise image data, sound data, textual data, or acombination of these forms. Additionally or alternatively, database(s)3760 may be configured to store privacy mode settings for wearableapparatus 110 associated with the one or more predetermined privacy modetriggers. The stored information relating to privacy mode settings maycomprise image data, sound data, textual data, or a combination of theseforms. The information may be preserved in a form that is readilyreadable, accessible, and/or executable by one or more of databaseaccess module 3750 and action execution module 3740.

Image analysis module 3710, audio analysis module 3720, image alterationmodule 3730, action execution module 3740, database access module 3750,and/or database(s) 3760 may be implemented in software, hardware,firmware, a mix of any of those, or the like. For example, if themodules are implemented in software, they may be stored in memory550/550 a/550 b, as shown in FIG. 37. Other components of apparatus 110may be configured to perform processes to implement and facilitateoperations of the modules. Thus, image analysis module 3710, audioanalysis module 3720, image alteration module 3730, action executionmodule 3740, database access module 3750, and/or database(s) 3760 mayinclude software, hardware, or firmware instructions (or a combinationthereof) executable by one or more processors (e.g., processor 210/540),alone or in various combinations with each other. For example, themodules may be configured to interact with each other and/or othermodules of wearable apparatus 110 to perform functions consistent withdisclosed embodiments. In some embodiments, any of the disclosed modules(e.g., image analysis module 3710, audio analysis module 3720, imagealteration module 3730, action execution module 3740, database accessmodule 3750, and/or database(s) 3760) may each include dedicated sensors(e.g., IR, image sensors, etc.) and/or dedicated application processingdevices to perform the functionality associated with each module.

As used herein, real-time image data may refer to image data captured inreal-time or near real-time. For example, image analysis module 3710 maymonitor the field of view of wearable apparatus 110 to detect inputswhile one or more of image alteration module 3730 and action executionmodule 3740 may determine whether to initiate one or more actions.Accordingly, image analysis module 3710, image alteration module 3730,and action execution module 3740 may operate in parallel or in anycombination to process image data received from image sensor 220. Thatis, wearable apparatus 110 may capture and analyze image data via imagesensor 220 in parallel, or may institute a queue-like implementationwhereby image data is captured and then analyzed in a continuous fashion(i.e., a first image is captured and analyzed while a subsequent imageis captured and then subsequently analyzed).

Consistent with disclosed embodiments, wearable apparatus 110 may have aprivacy mode, with associated automatically variable privacy settings.When the privacy mode is enacted, such as by detection of a privacy modetrigger, wearable apparatus 110 may, depending on the associated privacysettings, execute one or more actions such as stopping or suspendingcapturing images of the environment of user 100, stopping or suspendingcapturing sounds from the environment of user 100, stopping orsuspending storing captured images, stopping or suspending storingcaptured sounds, or other actions. The devices and methods discussedunder this heading may be combined with any of the devices and/ormethods discussed above and below.

In some embodiments, the privacy mode may be associated with one or moreprivate contextual situations. The private contextual situation may be asituation where the privacy of a person or a plurality of persons is ofconcern, including that of user 100. These situations may make itinappropriate for wearable apparatus 110 to capture images or soundsincluding or associated with the person or persons. In theseembodiments, one or more privacy mode triggers may be associated with aparticular private contextual situation. The privacy mode triggers maybe of a visual, audio, or textual nature. In these embodiments, eachprivacy mode trigger may be associated with one or more privacy modesettings that may be automatically adjusted by wearable apparatus 110and associated components to protect the privacy of the person orpersons. Adjustment of the privacy mode settings may, as non-limitingexamples, alter captured images, suspend storage or capture of images orsounds, etc.

Visual privacy mode triggers associated with particular privatecontextual situations may be predefined, and may include, but not belimited to, entry into a bathroom, exit from a bathroom, entry into aprivate zone, exit from a private zone, a child, nudity, a signprohibiting recording, a tag associated with a limitation on recording,a predefined hand gesture, a restroom sign, a toilet, and/or a face ofan individual. As an example, a predefined hand gesture comprising avisual privacy mode trigger may include a hand gesture from anindividual within a field of view of wearable apparatus 110 and imagesensor 120 to stop capturing image of the person. In some embodiments,being near or in the restroom or toilet may be a private contextualsituation. In some embodiments, nudity of a person or a particular partof the human body being within the field of view of wearable apparatus110 and image sensor 220 may be a private contextual situation.

Audio privacy mode triggers associated with particular privatecontextual situations may be predefined, and may include, but not belimited to, sounds associated with a bathroom or a toilet, an indicatorof a confidential conversation, an indicator of a desire of anindividual not to be recorded, an indicator of an individual warningthat the scene or other information should not be recorded, etc.

The visual and audio privacy mode triggers may be stored in database(s)3760, as described above. Associated privacy mode settings may also bestored in database(s) 3760, and may be stored with or otherwiseassociated with one or more particular privacy mode triggers. Forexample, folders or other organizational techniques may be used to groupall privacy mode triggers and privacy mode settings for a particularprivate contextual situation.

In some embodiments, once wearable apparatus 110, image sensor 220,and/or audio sensor 3610 detect that a private contextual situationexists, the privacy mode settings may be automatically varied by thedisclosed components and modules consistent with disclosed embodiments.In some embodiments, disclosed components and modules, consistent withdisclosed embodiments, may revert the privacy mode settings to theirprior state and may resume normal operation once the private contextualsituation is determined to no longer exist.

FIGS. 38A-C show example environments that user 100 and wearableapparatus 110 may encounter, consistent with disclosed embodiments.FIGS. 38A-C are depicted as example fields of view that may be perceivedby user 100 through an wearable apparatus 110 equipped with an imagesensor 220. In some embodiments, the wearable apparatus 110 may beassociated with a pair of glasses 130 as discussed above. In otherembodiments, wearable apparatus 110 may be attached to clothing worn byuser 100, or may otherwise be associated with user 100's body. In someembodiments, wearable apparatus 110 may be associated or affixed to aremote structure, including but not limited to a stick, a pole, a drone,a vehicle, a robot, or another such unit.

FIG. 38A depicts an example environment involving a bathroom, which maybe associated with a private contextual situation. The environment ofuser 100 may, for example include a restroom, signified in FIG. 38A byrestroom door 3810. In other embodiments, there may be a sign or othervisual indicator, or a toilet. In still other embodiments, there may bean audio indicator, such as a flushing toilet, operation of a sink, ahand dryer, etc. After identifying restroom door 3810 from an imagecaptured by wearable apparatus 110 and image sensor 220, or otherwiseidentifying associated sounds via audio sensor 3610, processor 210/540may compare the image of restroom door 3810 with the visual triggersstored in database(s) 3760, and/or the detected sounds with the audiotriggers stored in database(s) 3760, as will be described in furtherdetail below in association with process 3900. Based on operations andfeedback associated with one or more software modules stored in memory550/550 a/550 b, processor 210/540 may determine that image sensor 220and/or audio sensor 3610 is capturing an image or sound of a toilet orrestroom, which indicates that there is a private contextual situationwithin the field of view of user 100 and wearable apparatus 110.Wearable apparatus 110 may stop, suspend, or otherwise alter image andsound data capture of the environment including the restroom. When user100 walks away from the restroom such that the images no longer indicatethe presence of the private contextual situation (such as restroom door3810), and/or the sounds no longer indicate the situation, wearableapparatus 110 may resume normal capture and/or storage of the images andsounds of the environment of user 100.

FIG. 38B depicts an example environment involving an individual 3820,which may be associated with a private contextual situation. Theenvironment of user 100 may include individual 3820. In someembodiments, individual 3820 may perceive the presence of user 100, andmay further perceive that user 100 is in possession of and/or isoperating wearable apparatus 110. In some embodiments, individual 3820may provide an audible warning to user 100 to stop operation of wearableapparatus 110, such as audible warning 3830 (“TURN IT OFF!”). In someembodiments, audible warning 3830 may not be present. In someembodiments, individual 3820 may not perceive the presence of user 100and/or may not perceive that user 100 is in possession of and/or isoperating wearable apparatus 110. After identifying individual 3820 froman image captured by wearable apparatus 110 and image sensor 220, andoptionally identifying associated sounds such as audible warning 3830via audio sensor 3610, processor 210/540 may compare the image ofindividual 3820 with the visual triggers stored in database(s) 3760,and/or the detected sounds such as audible warning 3830 with the audiotriggers stored in database(s) 3760, as will be described in furtherdetail below in association with process 3900. Based on operations andfeedback associated with one or more software modules stored in memory550/550 a/550 b, processor 210/540 may determine that image sensor 220and/or audio sensor 3610 is capturing an image or sound indicating aprivate contextual situation within the field of view of user 100 andwearable apparatus 110 that may be associated with individual 3820, orwith another individual, or with the scene generally. Wearable apparatus110 may stop, suspend, or otherwise alter image and sound data captureof the environment. When user 100 walks away from the scene (and/or fromindividual 3820) such that the images no longer indicate the presence ofthe private contextual situation and/or the sounds no longer indicatethe situation, wearable apparatus 110 may resume normal capture and/orstorage of the images and sounds of the environment of user 100.

FIG. 38C depicts an example environment involving an environmenttypically associated with a private contextual situation; here, acourtroom. The environment of user 100 may include visual indicators ofa courtroom, such as judge 3840. In some embodiments, judge 3840 mayperceive the presence of user 100, and may further perceive that user100 is in possession of and/or is operating wearable apparatus 110. Insome embodiments, judge 3840 may provide an audible warning to user 100to stop operation of wearable apparatus 110. In some embodiments, noaudible warning may be present. In some embodiments, judge 3840 may notperceive the presence of user 100 and/or may not perceive that user 100is in possession of and/or is operating wearable apparatus 110. In someembodiments, further visual or audio indicia present within theenvironment of user 100 may indicate a private contextual situation,such as sign 3850 (“NO AUDIO OR VISUAL RECORDING”). After identifyingobjects such as judge 3840 and sign 3850 from an image captured bywearable apparatus 110 and image sensor 220, and optionally identifyingassociated sounds such as an audible warning from judge 3840 via audiosensor 3610, processor 210/540 may compare the image(s) with the visualtriggers stored in database(s) 3760, and/or the detected sound(s) withthe audio triggers stored in database(s) 3760, as will be described infurther detail below in association with process 3900. Based onoperations and feedback associated with one or more software modulesstored in memory 550/550 a/550 b, processor 210/540 may determine thatimage sensor 220 and/or audio sensor 3610 is capturing an image or soundindicating a private contextual situation within the field of view ofuser 100 and wearable apparatus 110 that may be associated with thecourtroom (or similar environment/situation). Wearable apparatus 110 maystop, suspend, or otherwise alter image and sound data capture of theenvironment. When user 100 walks away from the scene (i.e., leaves thecourtroom, or court adjourns) such that the images no longer indicatethe presence of the private contextual situation and/or the sounds nolonger indicate the situation, wearable apparatus 110 may resume normalcapture and/or storage of the images and sounds of the environment ofuser 100.

FIG. 39 is a flowchart illustrating an example of a process 3900 forautomatically varying settings associated with a privacy mode for animage sensor associated with a wearable apparatus, consistent withdisclosed embodiments. Process 3900, as well as any or all of theindividual steps therein, may be performed by various aspects ofwearable apparatus 110, such as processor 210/540, image sensor 220,audio sensor 3610, image analysis module 3710, audio analysis module3720, image alteration module 3730, action execution module 3740,database access module 3750, and database(s) 3760, or any subcomponentstherein. In some embodiments, one or more steps of process 3900 may beperformed by a remote computing system, such as computing system 120 orserver 250. For exemplary purposes, FIG. 39 and process 3900 aredescribed as being performed by processor 210, executing softwareinstructions stored within memory 550.

Processor 210 may execute software instructions via image analysismodule 3710 that enable wearable apparatus 110 to capture real-timeimage data from the environment of a user 100 using a camera associatedwith an image sensor, such as image sensor 220 (Step 3910). In someembodiments, the captured first set of real-time image data may bereceived as a single streaming video file. In other embodiments, thereal-time image data may be received as a series of still images. Whenthe captured image data is received, processor 210 may store the data inmemory 550. In some embodiments, the image data may be processed inreal-time and not stored. In some embodiments, wearable apparatus 110may additionally or alternatively via processor 210 execute softwareinstructions via audio analysis module 3720 that enable wearableapparatus 110 to capture real-time audio data from the environment of auser 100 using an audio sensor 3610, for example, a microphone. When thecaptured audio data is received, processor 210 may store the data inmemory 550. In some embodiments, the audio data may be processed inreal-time and not stored.

Processor 210 may execute software instructions via one or more of imageanalysis module 3710 and/or audio analysis module 3720 that enablewearable apparatus 110 to detect that user 100 has perceived thepresence of a privacy mode trigger (Step 3920). As discussed above, theterm “privacy mode trigger” includes any information in the image dataor audio data that may be associated with a private contextualsituation. Privacy mode triggers may be visual and/or audio in nature.Examples of visual privacy mode triggers that may be detected bywearable apparatus 110 through image sensor 220 and image analysismodule 3710 may include, but not be limited to, a presence in the one ormore of the plurality of images captured in step 3910 of featuresassociated with a bathroom; entry into a bathroom; exit from a bathroom;entry into a private zone; exit from a private zone; a child; nudity; asign prohibiting recording; a tag associated with a limitation onrecording; or an appearance in the one or more of the plurality ofimages a representation of a recognized individual. Examples of audioprivacy mode triggers that may be detected by wearable apparatus 110through audio sensor 3610 and audio analysis module 3720 may include,but not be limited to, an indicator of a confidential conversation; anindicator of a desire of an individual not to be recorded; or thepresence of the voice of a recognized individual. In some embodiments,step 3920 may be performed by a remote computing system, such ascomputing system 120 or server 250.

In some embodiments, wearable apparatus 110 may include a transmitter,such as wireless transceiver 530, and processor 210 may be configured totransmit image and/or audio data (either stored data or in real time) toa remote system such as computing system 120 or server 250 for purposesof analyzing the image and/or audio data to determine whether a privacymode trigger is present in the data. In other embodiments, processor 210may be configured not to transmit the data, and may instead executesoftware instructions stored on memory 550, such as image analysismodule 3710, audio analysis module 3720, image alteration module 3730,action execution module 3740, database access module 3750, anddatabase(s) 3760.

These modules and/or databases may further be executed to analyzeinformation related to the privacy mode trigger, such as associatedprivacy mode settings (Step 3930). For example, database access module3750 may receive information from one or more of image analysis module3710 and/or audio analysis module 3720 relating to a detected privacymode trigger and/or private contextual situation. Database access module3750 may be configured to search database(s) 3760 for one or moreentries associated with one or more privacy mode settings for thepredefined, recognized privacy mode trigger. In some embodiments, if thedetected privacy mode trigger is not one of the predefined privacy modetriggers with information stored in database(s) 3760, user 100 may beprompted by wearable apparatus 110 or computing system 120 throughdisplay 260 to configure privacy mode settings associated with the newprivacy mode trigger.

Database access module 3750 may determine one or more actions associatedwith the privacy mode settings for the detected privacy mode triggerthat, when executed by processor 210, may automatically cause one ormore adjustments to wearable apparatus 110 based on those settings (Step3940). In some embodiments, step 3940 may be performed by a remotecomputing system, such as computing system 120 or server 250. The actionand/or adjustment associated with a particular privacy mode trigger maybe defined by software instructions or other information associated withthe privacy mode settings found within database(s) 3760, and may betailored in a manner to be unique to a particular privacy mode triggerand private contextual situation.

For example, if a privacy mode trigger is detected by image analysismodule 3710 in image data captured via image sensor 220, theaction/adjustment may include one or more of causing a suspension ofimage capture by image sensor 220 or causing a suspension of storage ofimages captured by image sensor 220. In embodiments such as thatdepicted in FIGS. 36A-36C where wearable apparatus 110 includes atransmitter, such as wireless transceiver 530, the action/adjustment mayinclude a limitation placed on a type of information transmitted to apaired device, for example, computing system 120. In some embodiments,the action/adjustment may include enabling capturing of images by imagesensor 220, but not storing them, such as in memory 550 or database(s)3760. In other embodiments, the action/adjustment may includeprohibiting transmission of captured images. In still other embodiments,the action/adjustment may include transmission of caricature or cartoonrepresentations of captured images from image sensor 220, ortransmission of low resolution, distorted, and/or blurred versions ofcaptured images. In other embodiments, the action/adjustment may includeapplying an image filter to images captured via image sensor 220,including but not limited to applying a convolution model filter. Insome embodiments, the action/adjustment may include prohibiting theposting of captured images on social media. In other embodiments, theaction/adjustment may include blockage of at least part of anapplication program interface (“API”) functionality; for example, if asecret product or idea is being discussed, the device may prohibitmisappropriation of the idea or visual/audio representations thereofthrough third party software programs.

In some embodiments, the privacy mode trigger may include an appearancein the one or more of the plurality of images captured from image sensor220 of a representation of a recognized individual, as determined bydatabase access module 3750 and database(s) 3760. In these embodiments,a determined action/adjustment associated with a privacy mode settingfor these triggers may include causing transmission (such as viawireless transceiver 530) of information indicative of the identity ofthe individual (such as individual 3820 or judge 3840) to a paireddevice, such as computing system 120. In these embodiments, processor210 may ensure that the one or more of the plurality of imagesthemselves are not transmitted to the paired device. In theseembodiments, the system and incorporated components enable the paireddevice to execute one or more functions relating to the detectedindividual without receiving the one or more images. This configurationprotects the privacy of the individual and of user 100. In theseembodiments, the executable function may include, but not be limited to,displaying information relating to the individual, such as on display260.

In other embodiments, if a privacy mode trigger is detected by audioanalysis module 3720 in audio data captured via audio sensor 3610, theaction/adjustment may include one or more of suspending capture of audioinformation from audio sensor 3610 (e.g., a microphone), or suspendingtransmission of audio information from audio sensor 3610.

Via one or more of image alteration module 3730 and/or action executionmodule 3740, processor 210 may be configured to execute the determinedaction relating to the privacy mode settings so as to automaticallycause one or more adjustments to wearable apparatus 110 (Step 3950). Insome embodiments, Step 3950 may be performed by a remote computingsystem, such as computing system 120 or server 250. The actions may beany of the actions described above or below, as defined by theinformational content of the privacy mode settings associated with thedetected privacy mode trigger. In some embodiments, feedback relating tothe action/adjustment may be generated via feedback outputting unit 230and displayed to user 100 via a paired device, such as via display 260of computing system 120.

In some embodiments, processor 210, via image alteration module 3730,may be further programmed to identify one or more portions of at leastone image from a plurality of images captured by image sensor 220. Inthese embodiments, the presence of a particular privacy mode trigger maynecessitate different adjustments or actions; similarly, if multipleprivacy mode triggers are present, multiple actions/adjustments may benecessary. Based on the presence of the privacy mode trigger(s), imagealteration module 3730 may identify at least a first portion and asecond portion of the image, and may process the first portion using afirst processing scheme, and process the second portion using a secondprocessing scheme. The first and second processing schemes may comprisesoftware instructions that include or are associated with the privacymode settings stored in database(s) 3760 for a given privacy modetrigger and private contextual situation.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, Ultra HD Blu-ray, orother optical drive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. The various programsor program modules can be created using any of the techniques known toone skilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

What is claimed is:
 1. A wearable apparatus for identifying a contextualsituation related to a wearer, the wearable apparatus comprising: awearable image sensor configured to capture a plurality of images froman environment of the wearer; a transmitter; and at least one processingdevice programmed to: analyze the plurality of images to identify thecontextual situation related to the wearer; determine informationassociated with the contextual situation; and cause the transmitter totransmit the determined information to a device paired with the wearableapparatus to cause the paired device to provide at least one alert tothe wearer based on the determined information associated with thecontextual situation.
 2. The wearable apparatus of claim 1, wherein thecontextual situation includes the wearer transitioning from indoors tooutdoors.
 3. The wearable apparatus of claim 2, wherein the at least onealert includes information related to a weather condition.
 4. Thewearable apparatus of claim 2, wherein the at least one alert includes asuggestion to remember rain gear.
 5. The wearable apparatus of claim 2,wherein the at least one alert includes a suggestion to remember a key.6. The wearable apparatus of claim 2, wherein the at least one alertincludes a suggestion to change an HVAC setting.
 7. The wearableapparatus of claim 1, wherein the contextual situation includes thewearer entering a grocery store.
 8. The wearable apparatus of claim 7,wherein the at least one alert includes a suggestion to purchase anitem.
 9. The wearable apparatus of claim 8, wherein the suggestion topurchase an item is based on a prior determination by the at least oneprocessing device, based on prior captured images, that a containerassociated with the item was discarded by the wearer.
 10. The wearableapparatus of claim 1, wherein the contextual situation includes thewearer exiting a vehicle.
 11. The wearable apparatus of claim 10,wherein the at least one alert includes a reminder indicating that achild is present in the vehicle.
 12. The wearable apparatus of claim 1,wherein the contextual situation includes an identification of at leastone worker at a work site and the at least one alert indicates that theworker is not using and/or wearing safety equipment.
 13. The wearableapparatus of claim 1, wherein the paired device is at least one of: asmartphone, a smarthome controller, a tablet, or a smart watch.
 14. Thewearable apparatus of claim 1, wherein the contextual situation includesa document present in an area in front of the wearer.
 15. The wearableapparatus of claim 14, wherein the determined information includes anaddress appearing on the document.
 16. The wearable apparatus of claim15, wherein the at least one alert includes the address appearing in auser interface associated with a navigation assistance application. 17.The wearable apparatus of claim 14, wherein the determined informationincludes text from a business card.
 18. The wearable apparatus of claim17, wherein the at least one alert includes the text from the businesscard appearing in a user interface associated with an application on thepaired device.
 19. The wearable apparatus of claim 14, wherein thedetermined information includes text from a financial document.
 20. Thewearable apparatus of claim 19, wherein the at least one alert includestext from the financial document appearing in a user interfaceassociated with a financial-related application on the paired device.21. The wearable apparatus of claim 1, wherein the at least one alert isassociated with at least one of a determined reading speed of the user,a last page read of a book, or progress toward a predetermined readinggoal.
 22. The wearable apparatus of claim 1, wherein the at least oneprocessing device is further programmed to: after the transmission ofthe determined information, capture at least one image using thewearable image sensor; analyze the at least one image to identify asecond contextual situation related to the wearer; determine a timedifference between the contextual situation and the second contextualsituation; and based on the contextual situation, the second contextualsituation, and the determined time difference, withhold transmissionassociated with the second contextual situation to the paired device.23. A method for identifying a contextual situation related to a wearerof a wearable apparatus, the method comprising: receiving a plurality ofimages captured from an environment of the wearer; analyzing theplurality of images to identify the contextual situation related to thewearer; determining information associated with the contextualsituation; and causing a device paired with the wearable apparatus toprovide at least one alert to the wearer based on the determinedinformation associated with the contextual situation.
 24. The method ofclaim 23, further comprising: after causing the device paired with thewearable apparatus to provide at least one alert, receiving at least oneimage captured from an environment of the wearer; analyzing the at leastone image to identify a second contextual situation related to thewearer; determining a time difference between the contextual situationand the second contextual situation; and based on the contextualsituation, the second contextual situation, and the determined timedifference, withhold alerts associated with the second contextualsituation.
 25. A software product stored on a non-transitory computerreadable medium and comprising data and computer implementableinstructions for carrying out the method of claim 23.